Merge branch 'feature-ev' into development

.gitattributes

@@ -1,4 +1,6 @@
 *.go		diff=golang
 *.md		diff=markdown
-*.vimrc		linguist-language=vim
+*.vimrc		linguist-language=Vim Script
 *.star		linguist-language=Starlark
+
+bench/cec2020/input_data/* linguist-vendored

algo/algo.go

@@ -9,7 +9,10 @@
 	"sort"
 	"sync"
 
+	"git.dotya.ml/wanderer/math-optim/algo/cec2020"
+	"git.dotya.ml/wanderer/math-optim/algo/de"
 	"git.dotya.ml/wanderer/math-optim/bench"
+	c20 "git.dotya.ml/wanderer/math-optim/bench/cec2020"
 	"git.dotya.ml/wanderer/math-optim/report"
 	"git.dotya.ml/wanderer/math-optim/stats"
 )
@@ -37,6 +40,13 @@ func getComparisonOfMeansPics() []report.Pic {
 	return comparisonOfMeansPicList.Pics
 }
 
+// saveAlgoMeans saves algo bench means safely.
+func saveAlgoMeans(sabm stats.AlgoBenchMean) {
+	mu.Lock()
+	meanStats.AlgoMeans = append(meanStats.AlgoMeans, sabm)
+	mu.Unlock()
+}
+
 // GetMeanStats returns a pointer of type stats.MeanStats to a sorted package
 // global 'meanStats'.
 func GetMeanStats() *stats.MeanStats {
@@ -90,7 +100,8 @@ func PrepComparisonOfMeans(wg *sync.WaitGroup) (*report.PicList, int) {
 
 	algoCount := len(algos)
 	dimLen := len(bench.Dimensions)
-	benchCount := len(bench.Functions)
+	// without Rastrigin in active duty for the moment.
+	benchCount := len(bench.Functions) - 1
 
 	// note: this is a wee bit ugly.
 	for d := 0; d < dimLen; d++ {
@@ -142,7 +153,8 @@ func DoRandomSearch(wg *sync.WaitGroup, m *sync.Mutex) {
 	printRandomSearch("starting...")
 
 	// funcCount is the number of bench functions available.
-	funcCount := len(bench.Functions)
+	// without Rastrigin in active duty for the moment.
+	funcCount := len(bench.Functions) - 1
 	// stats for the current algo (RandomSearch).
 	algoStats := make([][]stats.Stats, funcCount)
 	// ch serves as a way to get the actual computed output.
@@ -214,7 +226,8 @@ func DoStochasticHillClimbing(wg *sync.WaitGroup, m *sync.Mutex) {
 	printSHC("starting...")
 
 	// funcCount is the number of bench functions available.
-	funcCount := len(bench.Functions)
+	// without Rastrigin in active duty for the moment.
+	funcCount := len(bench.Functions) - 1
 	// stats for the current algo (StochasticHillClimber).
 	algoStats := make([][]stats.Stats, funcCount)
 	// ch serves as a way to get the actual computed output.
@@ -275,7 +288,8 @@ func DoStochasticHillClimbing100Neigh(wg *sync.WaitGroup, m *sync.Mutex) {
 	printSHC("starting...")
 
 	// funcCount is the number of bench functions available.
-	funcCount := len(bench.Functions)
+	// without Rastrigin in active duty for the moment.
+	funcCount := len(bench.Functions) - 1
 	// stats for the current algo (StochasticHillClimber).
 	algoStats := make([][]stats.Stats, funcCount)
 	// ch serves as a way to get the actual computed output.
@@ -321,3 +335,161 @@ funcCount := len(bench.Functions)
 	stats.SaveTable(algoName, algoStats)
 	m.Unlock()
 }
+
+func DojDE(wg *sync.WaitGroup, m *sync.Mutex) {
+	defer wg.Done()
+
+	de.LogPrintln("starting")
+
+	// funcCount is the number of bench functions available and tested.
+	funcCount := len(bench.Functions)
+	// stats for the current algo.
+	algoStats := make([][]stats.Stats, funcCount)
+	// ch serves as a way to get the actual computed output.
+	ch := make(chan []stats.Stats, funcCount)
+	// chAlgoMeans := make(chan *stats.AlgoBenchMean, 1)
+	chAlgoMeans := make(chan *stats.AlgoBenchMean, funcCount)
+
+	defer close(ch)
+	defer close(chAlgoMeans)
+
+	// jDE params.
+	np := 50
+	f := 0.5
+	cr := 0.9
+
+	for i := range algoStats {
+		jDE := de.NewjDE()
+
+		// params:
+		//  Generations, minimum bench iterations, mutation strategy, parameter
+		//  self-adaptation scheme, initial population size, differential
+		//  weight, mutation constant, dimensions, bench name and a
+		//  synchronisation channel.
+		//
+		//  -1 to disable generation limits,
+		//  n > 0 for minimum bench iterations
+		//  0..17 to choose a mutation strategy,
+		//  0..1 to select a parameter self-adaptation scheme,
+		//  np >= 4 as initial population size.
+		jDE.Init(-1, 30, 0, 0, np, f, cr, bench.DimensionsGA, bench.FuncNames[i], ch, chAlgoMeans)
+
+		go jDE.Run()
+	}
+
+	// get results.
+	for i := range algoStats {
+		s := <-ch
+		aM := <-chAlgoMeans
+
+		algoStats[i] = s
+
+		saveAlgoMeans(*aM)
+	}
+
+	pCh := make(chan report.PicList, funcCount*len(bench.DimensionsGA))
+	pMeanCh := make(chan report.PicList, funcCount*len(bench.DimensionsGA))
+
+	for _, algoStat := range algoStats {
+		go plotAllDims(algoStat, "plot", ".pdf", pCh, pMeanCh)
+	}
+
+	pLs := []report.PicList{}
+	pLsMean := []report.PicList{}
+
+	for range algoStats {
+		pL := <-pCh
+		pLMean := <-pMeanCh
+
+		pLs = append(pLs, pL)
+		pLsMean = append(pLsMean, pLMean)
+	}
+
+	algoName := "Differential Evolution"
+
+	// protect access to shared data.
+	m.Lock()
+	report.SavePicsToFile(pLs, pLsMean, algoName)
+
+	stats.SaveTable(algoName, algoStats)
+	m.Unlock()
+}
+
+func DoCEC2020jDE(wg *sync.WaitGroup, m *sync.Mutex) {
+	defer wg.Done()
+
+	cec2020.LogPrintln("starting")
+
+	// funcCount is the number of bench functions available and tested.
+	funcCount := len(c20.Functions)
+	// stats for the current algo.
+	algoStats := make([][]stats.Stats, funcCount)
+	// ch serves as a way to get the actual computed output.
+	ch := make(chan []stats.Stats, funcCount)
+	// chAlgoMeans := make(chan *stats.AlgoBenchMean, 1)
+	chAlgoMeans := make(chan *stats.AlgoBenchMean, funcCount)
+
+	defer close(ch)
+	defer close(chAlgoMeans)
+
+	// jDE params.
+	np := 50
+	f := 0.5
+	cr := 0.9
+
+	for i := range algoStats {
+		jDE := cec2020.NewjDE()
+
+		// params:
+		//  Generations, minimum bench iterations, mutation strategy, parameter
+		//  self-adaptation scheme, initial population size, differential
+		//  weight, mutation constant, dimensions, bench name and a
+		//  synchronisation channel.
+		//
+		//  -1 to disable generation limits,
+		//  n > 0 for minimum bench iterations
+		//  0..17 to choose a mutation strategy,
+		//  0..1 to select a parameter self-adaptation scheme,
+		//  np >= 4 as initial population size.
+		jDE.Init(-1, 30, 0, 0, np, f, cr, c20.Dimensions, c20.FuncNames[i], ch, chAlgoMeans)
+
+		go jDE.Run()
+	}
+
+	// get results.
+	for i := range algoStats {
+		s := <-ch
+		aM := <-chAlgoMeans
+
+		algoStats[i] = s
+
+		saveAlgoMeans(*aM)
+	}
+
+	pCh := make(chan report.PicList, funcCount*len(c20.Dimensions))
+	pMeanCh := make(chan report.PicList, funcCount*len(c20.Dimensions))
+
+	for _, algoStat := range algoStats {
+		go plotAllDims(algoStat, "plot", ".pdf", pCh, pMeanCh)
+	}
+
+	pLs := []report.PicList{}
+	pLsMean := []report.PicList{}
+
+	for range algoStats {
+		pL := <-pCh
+		pLMean := <-pMeanCh
+
+		pLs = append(pLs, pL)
+		pLsMean = append(pLsMean, pLMean)
+	}
+
+	algoName := "Self-adapting Differential Evolution"
+
+	// protect access to shared data.
+	m.Lock()
+	report.SavePicsToFile(pLs, pLsMean, algoName)
+
+	stats.SaveTable(algoName, algoStats)
+	m.Unlock()
+}

algo/cec2020/doc.go

@@ -0,0 +1,5 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+// Package cec2020 implements algorithms required by CEC2020: jDE and T3A.
+package cec2020

algo/cec2020/jDE.go

@@ -0,0 +1,500 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package cec2020
+
+import (
+	"sort"
+	"time"
+
+	"golang.org/x/exp/rand"
+
+	"git.dotya.ml/wanderer/math-optim/bench/cec2020"
+	"git.dotya.ml/wanderer/math-optim/stats"
+	"gonum.org/v1/gonum/stat/distuv"
+)
+
+// JDE is a holder for the settings of an instance of a self-adapting
+// differential evolution (jDE) algorithm.
+type JDE struct {
+	// Generations denotes the number of generations the population evolves
+	// for. Special value -1 disables limiting the number of generations.
+	Generations int
+	// BenchMinIters is the number of iterations the bench function will be re-run (for statistical purposes).
+	BenchMinIters int
+	// Dimensions to solve the problem for.
+	Dimensions []int
+	// F is the initial value of the differential weight (mutation/weighting factor).
+	F float64
+	// CR is the initial value of the crossover probability constant.
+	CR float64
+	// rngF is a random number generator for differential weight (mutation/weighting factor).
+	rngF distuv.Uniform
+	// cr is a random number generator for crossover probability constant adapted over time.
+	rngCR distuv.Uniform
+	// MutationStrategy selects the mutation strategy, i.e. the variant of the
+	// jDE algorithm (0..17), see mutationStrategies.go for more details.
+	MutationStrategy int
+	// AdptScheme is the parameter self-adaptation scheme (0..1).
+	AdptScheme int
+	// NP is the initial population size.
+	NP int
+	// BenchName is a name of the problem to optimise.
+	BenchName string
+	// ch is a channel for writing back computed results.
+	ch chan []stats.Stats
+	// chAlgoMeans is a channel for writing back algo means.
+	chAlgoMeans chan *stats.AlgoBenchMean
+	// initialised denotes the initialisation state of the struct.
+	initialised bool
+}
+
+const (
+	// jDEMinNP is the minimum size of the initial population for jDE.
+	// for jDE PaGMO specifies 8.
+	jDEMinNP = 4
+	// fMin is the minimum allowed value of the differential weight.
+	fMin = 0.36
+	// fMax is the maximum allowed value of the differential weight.
+	fMax = 1.0
+	// crMin is the minimum allowed value of the crossover probability constant.
+	crMin = 0.0
+	// crMax is the maximum allowed value of the crossover probability constant.
+	crMax = 1.0
+
+	// tau1 is used in parameter self-adaptation.
+	tau1 = 0.1
+	// tau2 is used in parameter self-adaptation.
+	tau2 = 0.1
+	// fl is used in parameter self-adaptation.
+	fl = 0.1
+	// fu is used in parameter self-adaptation.
+	fu = 0.9
+)
+
+// Init initialises the jDE algorithm, performs sanity checks on the inputs.
+func (j *JDE) Init(generations, benchMinIters, mutStrategy, adptScheme, np int, f, cr float64, dimensions []int, bench string, ch chan []stats.Stats, chAlgoMeans chan *stats.AlgoBenchMean) {
+	if j == nil {
+		cec2020Logger.Fatalln("jDE needs to be initialised before calling RunjDE, exiting...")
+	}
+
+	// check input parameters.
+	switch {
+	case generations == 0:
+		cec2020Logger.Fatalln("Generations cannot be 0, got", generations)
+
+	case generations == -1:
+		cec2020Logger.Println("Generations is '-1', disabling generation limits..")
+
+	case benchMinIters < 1:
+		cec2020Logger.Fatalln("Minimum bench iterations cannot be less than 1, got:", benchMinIters)
+
+	case mutStrategy < 0 || mutStrategy > 17:
+		cec2020Logger.Fatalln("Mutation strategy needs to be from the interval <0; 17>, got", mutStrategy)
+
+	case adptScheme < 0 || adptScheme > 1:
+		cec2020Logger.Fatalln("Parameter self-adaptation scheme needs to be from the interval <0; 1>, got", adptScheme)
+
+	case np < jDEMinNP:
+		cec2020Logger.Fatalf("NP cannot be less than %d, got: %d\n.", jDEMinNP, np)
+
+	case f < fMin || f > fMax:
+		cec2020Logger.Fatalf("F needs to be from the interval <%f;%f>, got: %f\n.", fMin, fMax, f)
+
+	case cr < crMin || cr > crMax:
+		cec2020Logger.Fatalf("CR needs to be from the interval <%f;>%f, got: %f\n.", crMin, crMax, cr)
+
+	case len(dimensions) == 0:
+		cec2020Logger.Fatalf("Dimensions cannot be empty, got: %+v\n", dimensions)
+
+	case bench == "":
+		cec2020Logger.Fatalln("Bench cannot be empty, got:", bench)
+	}
+
+	j.Generations = generations
+	j.BenchMinIters = benchMinIters
+	j.MutationStrategy = mutStrategy
+	j.AdptScheme = adptScheme
+	j.NP = np
+	j.F = f
+	j.CR = cr
+
+	rngsrc := rand.NewSource(uint64(rand.Int63()))
+
+	j.rngF = distuv.Uniform{Min: fMin, Max: fMax, Src: rngsrc}
+	j.rngCR = distuv.Uniform{Min: crMin, Max: crMax, Src: rngsrc}
+
+	j.Dimensions = dimensions
+	j.BenchName = bench
+	j.ch = ch
+	j.chAlgoMeans = chAlgoMeans
+
+	j.initialised = true
+
+	cec2020Logger.Printf("jDE init done, jDE:%+v", j)
+}
+
+// InitAndRun initialises the jDE algorithm, performs sanity checks on the
+// inputs and calls the Run method.
+func (j *JDE) InitAndRun(generations, benchMinIters, mutStrategy, adptScheme, np int, f, cr float64, dimensions []int, bench string, ch chan []stats.Stats, chAlgoMeans chan *stats.AlgoBenchMean) {
+	if j == nil {
+		cec2020Logger.Fatalln("jDE is nil, NewjDE() needs to be called first. exiting...")
+	}
+
+	j.Init(generations, benchMinIters, mutStrategy, adptScheme, np, f, cr, dimensions, bench, ch, chAlgoMeans)
+
+	j.Run()
+}
+
+// Run self-adapting differential evolution algorithm.
+func (j *JDE) Run() {
+	if j == nil {
+		cec2020Logger.Fatalln("jDE is nil, NewjDE() needs to be called first. exiting...")
+	}
+
+	if !j.initialised {
+		cec2020Logger.Fatalln("jDE needs to be initialised before calling Run(), exiting...")
+	}
+
+	var jDEStats []stats.Stats
+
+	jDEMeans := &stats.AlgoBenchMean{
+		Algo:       "jDE",
+		BenchMeans: make([]stats.BenchMean, 0, len(j.Dimensions)),
+	}
+
+	// run evolve for all dimensions.
+	for _, dim := range j.Dimensions {
+		maxFES := cec2020.GetMaxFES(dim)
+		fesPerIter := int(float64(maxFES / j.NP))
+		jDEStatDimX := &stats.Stats{
+			Algo:        "jDE",
+			Dimens:      dim,
+			Iterations:  j.BenchMinIters,
+			Generations: maxFES,
+			NP:          j.NP,
+			F:           j.F,
+			CR:          j.CR,
+		}
+		funcStats := &stats.FuncStats{BenchName: j.BenchName}
+		dimXMean := &stats.BenchMean{
+			Bench:       j.BenchName,
+			Dimens:      dim,
+			Iterations:  j.BenchMinIters,
+			Generations: maxFES,
+			Neighbours:  -1,
+		}
+		uniDist := distuv.Uniform{
+			Min: cec2020.SearchRange.Min(),
+			Max: cec2020.SearchRange.Max(),
+		}
+
+		if maxFES == -1 {
+			cec2020Logger.Fatalf("could not get maxFES for current dim (%d), bailing", dim)
+		}
+
+		cec2020Logger.Printf("running bench \"%s\" for %dD, maxFES: %d\n",
+			j.BenchName, dim, maxFES,
+		)
+
+		funcStats.BenchResults = make([]stats.BenchRound, j.BenchMinIters)
+
+		// rand.Seed(uint64(time.Now().UnixNano()))
+		// create and seed a source of pseudo-randomness
+		src := rand.NewSource(uint64(rand.Int63()))
+
+		// track execution duration.
+		start := time.Now()
+
+		for iter := 0; iter < j.BenchMinIters; iter++ {
+			cec2020Logger.Printf("run: %d, bench: %s, %dD, started at %s",
+				iter, j.BenchName, dim, start,
+			)
+
+			funcStats.BenchResults[iter].Iteration = iter
+
+			uniDist.Src = src
+
+			// create a population with known params.
+			pop := newPopulation(j.BenchName, j.NP, dim)
+
+			// set population seed.
+			pop.Seed = uint64(time.Now().UnixNano())
+			// initialise the population.
+			pop.Init()
+
+			var bestResult float64
+
+			// the core.
+			for i := 0; i < fesPerIter; i++ {
+				r := j.evaluate(pop)
+
+				// distinguish the first or any of the subsequent iterations.
+				switch i {
+				case 0:
+					bestResult = r
+
+				default:
+					// call evolve where jDE runs.
+					j.evolve(pop, &uniDist)
+
+					// take measurements of current population fitness.
+					r = j.evaluate(pop)
+
+					// save if better.
+					if r < bestResult {
+						bestResult = r
+					}
+				}
+
+				funcStats.BenchResults[iter].Results = append(
+					funcStats.BenchResults[iter].Results,
+					bestResult,
+				)
+				// this block makes sure we properly count func evaluations for
+				// the purpose of correctly comparable plot comparison. i.e.
+				// append the winning (current best) value NP-1 (the first best
+				// is already saved at this point) times to represent the fact
+				// that while evaluating (and comparing) other population
+				// individuals to the current best value is taking place in the
+				// background, the current best value itself is kept around and
+				// symbolically saved as the best of the Generation.
+				for x := 0; x < j.NP-1; x++ {
+					funcStats.BenchResults[iter].Results = append(
+						funcStats.BenchResults[iter].Results,
+						bestResult,
+					)
+				}
+			}
+		}
+
+		elapsed := time.Since(start)
+
+		cec2020Logger.Printf("completed: bench: %s, %dD, computing took %s\n",
+			j.BenchName, dim, elapsed,
+		)
+
+		// get mean vals.
+		dimXMean.MeanVals = stats.GetMeanVals(funcStats.BenchResults, maxFES)
+		funcStats.MeanVals = dimXMean.MeanVals
+
+		jDEStatDimX.BenchFuncStats = append(
+			jDEStatDimX.BenchFuncStats, *funcStats,
+		)
+
+		jDEStats = append(jDEStats, *jDEStatDimX)
+
+		jDEMeans.BenchMeans = append(jDEMeans.BenchMeans, *dimXMean)
+	}
+
+	sort.Sort(jDEMeans)
+
+	j.chAlgoMeans <- jDEMeans
+	j.ch <- jDEStats
+}
+
+// evaluate evaluates the fitness of current population.
+func (j *JDE) evaluate(pop *Population) float64 {
+	f := cec2020.Functions[pop.Problem]
+
+	bestIndividual := pop.Population[pop.GetBestIdx()].CurX
+	bestSolution := f(bestIndividual)
+
+	return bestSolution
+}
+
+// evolve evolves a population by running the jDE (self-adapting Differential
+// Evolution) algorithm on the passed population until termination conditions
+// are met.
+// nolint: gocognit
+func (j *JDE) evolve(pop *Population, uniDist *distuv.Uniform) {
+	popCount := len(pop.Population)
+
+	for i, currentIndividual := range pop.Population {
+		idcs := make([]int, 0, popCount-1)
+		// idcs := make([]int, popCount-1)
+
+		// gather indices.
+		for k := 0; k < popCount; k++ {
+			if k != i {
+				idcs = append(idcs, k)
+			}
+		}
+
+		// randomly choose 3 of those idcs.
+		selectedIdcs := make([]int, 0)
+
+		selectedA := false
+		selectedB := false
+		selectedC := false
+
+		for !selectedA {
+			candidateA := rand.Intn(len(idcs)) % len(idcs)
+
+			if candidateA != i {
+				selectedIdcs = append(selectedIdcs, candidateA)
+				selectedA = true
+			}
+		}
+
+		for !selectedB {
+			a := selectedIdcs[0]
+			candidateB := rand.Intn(len(idcs)) % len(idcs)
+
+			if candidateB != i && candidateB != a {
+				selectedIdcs = append(selectedIdcs, candidateB)
+				selectedB = true
+			}
+		}
+
+		for !selectedC {
+			a := selectedIdcs[0]
+			b := selectedIdcs[1]
+			candidateC := rand.Intn(len(idcs)) % len(idcs)
+
+			if candidateC != i && candidateC != a && candidateC != b {
+				selectedIdcs = append(selectedIdcs, candidateC)
+				selectedC = true
+			}
+		}
+
+		// selected contains the selected population individuals.
+		selected := make([]PopulationIndividual, 0)
+
+		// select individuals for rand/1/bin
+		for _, idx := range selectedIdcs {
+			for k := 0; k < popCount; k++ {
+				if k == idx {
+					selected = append(selected, pop.Population[idx])
+				}
+			}
+		}
+
+		mutant := make([]float64, pop.Dimen)
+
+		// mutate.
+		for k := 0; k < pop.Dimen; k++ {
+			// mutant = a + mut * (b - c)
+			mutant[k] = selected[0].CurX[k] + (pop.BestF * (selected[1].CurX[k] - selected[2].CurX[k]))
+
+			// clip values to <0;1>.
+			switch {
+			case mutant[k] < 0:
+				mutant[k] = 0
+
+			case mutant[k] > 1:
+				mutant[k] = 1
+			}
+		}
+
+		crossPoints := make([]bool, pop.Dimen)
+
+		// prepare crossover points (binomial crossover).
+		for k := 0; k < pop.Dimen; k++ {
+			if v := uniDist.Rand(); v < pop.BestCR {
+				crossPoints[k] = true
+			} else {
+				crossPoints[k] = false
+			}
+		}
+
+		trial := make([]float64, pop.Dimen)
+
+		// recombine using crossover points.
+		for k := 0; k < pop.Dimen; k++ {
+			if crossPoints[k] {
+				trial[k] = currentIndividual.CurX[k]
+			} else {
+				trial[k] = mutant[k]
+			}
+		}
+
+		f := cec2020.Functions[pop.Problem]
+		currentFitness := f(currentIndividual.CurX)
+		trialFitness := f(trial)
+
+		// replace if better.
+		if trialFitness < currentFitness {
+			pop.Population[i].CurX = trial
+		}
+	}
+
+	// adapt parameters.
+	j.adaptParameters(pop)
+}
+
+// adptParameters adapts parameters F and CR based on
+// https://labraj.feri.um.si/images/0/05/CEC09_slides_Brest.pdf.
+func (j *JDE) adaptParameters(p *Population) {
+	var nuF float64
+
+	var nuCR float64
+
+	switch {
+	case j.AdptScheme == 0:
+		if rand2 := j.getRandF(); rand2 < tau1 {
+			nuF = fl + (j.getRandF() * fu)
+		} else {
+			nuF = p.f[len(p.f)-1]
+		}
+
+		if rand4 := j.getRandCR(); rand4 < tau2 {
+			nuCR = j.getRandCR()
+		} else {
+			nuCR = p.cr[len(p.cr)-1]
+		}
+
+	case j.AdptScheme == 1:
+		nuF = p.BestF + j.getRandF()*0.5
+		nuCR = p.BestCR + j.getRandCR()*0.5
+	}
+
+	// sort out F.
+	p.f = append(p.f, nuF)
+	// sort out CR
+	p.cr = append(p.cr, nuCR)
+
+	// update best, if improved.
+	switch {
+	case nuF < p.BestF:
+		p.BestF = nuF
+
+	case nuCR < p.BestCR:
+		p.BestCR = nuCR
+	}
+}
+
+// getRandF returns a random value of F for parameter self-adaptation.
+func (j *JDE) getRandF() float64 {
+	return j.rngF.Rand()
+}
+
+// getRandCR returns a random value of CR for parameter self-adaptation.
+func (j *JDE) getRandCR() float64 {
+	return j.rngCR.Rand()
+}
+
+// NewjDE returns a pointer to a new, uninitialised jDE instance.
+func NewjDE() *JDE {
+	return &JDE{}
+}
+
+// LogPrintln wraps the jDE logger's Println func.
+func LogPrintln(v ...any) {
+	cec2020Logger.Println(v...)
+}
+
+// LogPrintf wraps the jDE logger's Printf func.
+func LogPrintf(s string, v ...any) {
+	cec2020Logger.Printf(s, v...)
+}
+
+// LogFatalln wraps the jDE logger's Fatalln func.
+func LogFatalln(s string) {
+	cec2020Logger.Fatalln(s)
+}
+
+// LogFatalf wraps the jDE logger's Fatalf func.
+func LogFatalf(s string, v ...any) {
+	cec2020Logger.Fatalf(s, v...)
+}

algo/cec2020/log.go

@@ -0,0 +1,12 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package cec2020
+
+import (
+	"log"
+	"os"
+)
+
+// cec2020Logger declares and initialises a "custom" CEC2020 logger.
+var cec2020Logger = log.New(os.Stderr, " *** ∁ cec2020:", log.Ldate|log.Ltime|log.Lshortfile)

algo/cec2020/population.go

@@ -0,0 +1,167 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package cec2020
+
+import (
+	"git.dotya.ml/wanderer/math-optim/bench/cec2020"
+	"golang.org/x/exp/rand"
+
+	"gonum.org/v1/gonum/stat/distuv"
+)
+
+type (
+	// DecisionVector is a []float64 abstraction representing the decision vector.
+	DecisionVector []float64
+	// FitnessVector is a []float64 abstraction representing the fitness vector.
+	FitnessVector []float64
+	// ConstraintVector is a []float64 abstraction representing the constraint vector.
+	ConstraintVector []float64
+)
+
+// PopulationIndividual representats a single population individual.
+type PopulationIndividual struct {
+	CurX DecisionVector
+	CurV DecisionVector
+	CurC ConstraintVector
+	CurF FitnessVector
+
+	BestX DecisionVector
+	BestC ConstraintVector
+	BestF FitnessVector
+}
+
+// ChampionIndividual is a representation of the best individual currently
+// available in the population.
+type ChampionIndividual struct {
+	X DecisionVector
+	C ConstraintVector
+	F FitnessVector
+}
+
+// Population groups population individuals (agents) with metadata about the population.
+type Population struct {
+	// Population is a slice of population individuals.
+	Population []PopulationIndividual
+	// Problem is the current benchmarking function this population is attempting to optimise.
+	Problem string
+	// Dimen is the dimensionality of the problem being optimised.
+	Dimen int
+	// Seed is the value used to (re)init population.
+	Seed uint64
+
+	// f is the differential weight (mutation/weighting factor) adapted over time.
+	f []float64
+	// cr is the crossover probability constant adapted over time.
+	cr []float64
+	// BestF is the best recorded value of the differential weight F.
+	BestF float64
+	// BestCR is the best recorded value of the differential weight CR.
+	BestCR float64
+}
+
+// GetIndividual returns a reference to individual at position n.
+func (p *Population) GetIndividual(n uint) *PopulationIndividual { return &PopulationIndividual{} }
+
+// GetBestIdx returns the index of the best population individual.
+func (p *Population) GetBestIdx() int {
+	f := cec2020.Functions[p.Problem]
+
+	bestIndividual := 0
+	// the first one is the best one.
+	bestVal := f(p.Population[0].CurX)
+
+	for i, v := range p.Population {
+		current := f(v.CurX)
+
+		if current < bestVal {
+			bestIndividual = i
+		}
+	}
+
+	return bestIndividual
+}
+
+// GetWorstIdx returns the index of the worst population individual.
+func (p *Population) GetWorstIdx() int {
+	f := cec2020.Functions[p.Problem]
+
+	worstIndividual := 0
+	// the first one is the worst one.
+	worstVal := f(p.Population[0].CurX)
+
+	for i, v := range p.Population {
+		current := f(v.CurX)
+
+		if current > worstVal {
+			worstIndividual = i
+		}
+	}
+
+	return worstIndividual
+}
+
+// Init initialises all individuals to random values.
+func (p *Population) Init() {
+	uniform := distuv.Uniform{
+		Min: cec2020.SearchRange.Min(),
+		Max: cec2020.SearchRange.Max(),
+	}
+	uniform.Src = rand.NewSource(p.Seed)
+
+	for i, v := range p.Population {
+		v.CurX = make([]float64, p.Dimen)
+
+		for j := 0; j < p.Dimen; j++ {
+			v.CurX[j] = uniform.Rand()
+		}
+
+		p.Population[i] = v
+	}
+
+	p.f = make([]float64, p.Size())
+	p.cr = make([]float64, p.Size())
+}
+
+// Reinit reinitialises all individuals.
+func (p *Population) Reinit() {
+	p.Init()
+}
+
+// ReinitN reinitialises the individual at position n.
+func (p *Population) ReinitN(n uint) {}
+
+// Clear sets all vectors to 0.
+func (p *Population) Clear() {
+	if p.Population != nil {
+		for _, v := range p.Population {
+			v.CurX = make([]float64, p.Dimen)
+			v.CurC = make([]float64, p.Dimen)
+			v.CurF = make([]float64, p.Dimen)
+			v.CurV = make([]float64, p.Dimen)
+			v.BestX = make([]float64, p.Dimen)
+			v.BestC = make([]float64, p.Dimen)
+			v.BestF = make([]float64, p.Dimen)
+		}
+	}
+}
+
+// MeanVelocity computes the mean current velocity of all individuals in the population.
+func (p *Population) MeanVelocity() float64 { return 0.0 }
+
+// Size returns the number of population individuals.
+func (p *Population) Size() int { return len(p.Population) }
+
+// newPopulation returns a pointer to a new, uninitialised population.
+func newPopulation(benchProblem string, np, dimen int) *Population {
+	p := &Population{}
+
+	p.Problem = benchProblem
+
+	p.Dimen = dimen
+
+	// pre-alloc.
+	p.Population = make([]PopulationIndividual, np)
+
+	return p
+}

algo/de/doc.go

@@ -0,0 +1,6 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+// Package de contains implementation details of Differential Evolution kind of
+// algorithms.
+package de

algo/de/jDE.go

@@ -0,0 +1,424 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package de
+
+import (
+	"log"
+	"os"
+	"sort"
+	"time"
+
+	"golang.org/x/exp/rand"
+
+	"git.dotya.ml/wanderer/math-optim/bench"
+	"git.dotya.ml/wanderer/math-optim/stats"
+	"gonum.org/v1/gonum/stat/distuv"
+)
+
+// JDE is a holder for the settings of an instance of a self-adapting
+// differential evolution (jDE) algorithm.
+type JDE struct {
+	// Generations denotes the number of generations the population evolves
+	// for. Special value -1 disables limiting the number of generations.
+	Generations int
+	// BenchMinIters is the number of iterations the bench function will be re-run (for statistical purposes).
+	BenchMinIters int
+	// Dimensions to solve the problem for.
+	Dimensions []int
+	// F is the differential weight (mutation/weighting factor).
+	F float64
+	// CR is the crossover probability constant.
+	CR float64
+	// MutationStrategy selects the mutation strategy, i.e. the variant of the
+	// jDE algorithm (0..17), see mutationStrategies.go for more details.
+	MutationStrategy int
+	// AdptScheme is the parameter self-adaptation scheme (0..1).
+	AdptScheme int
+	// NP is the initial population size.
+	NP int
+	// BenchName is a name of the problem to optimise.
+	BenchName string
+	// ch is a channel for writing back computed results.
+	ch chan []stats.Stats
+	// chAlgoMeans is a channel for writing back algo means.
+	chAlgoMeans chan *stats.AlgoBenchMean
+	// initialised denotes the initialisation state of the struct.
+	initialised bool
+}
+
+const (
+	// jDEMinNP is the minimum size of the initial population for jDE.
+	jDEMinNP = 4
+	// fMin is the minimum allowed value of the differential weight.
+	fMin = 0.5
+	// fMax is the maximum allowed value of the differential weight.
+	fMax = 2.0
+	// crMin is the minimum allowed value of the crossover probability constant.
+	crMin = 0.2
+	// crMax is the maximum allowed value of the crossover probability constant.
+	crMax = 0.9
+)
+
+// jDELogger declares and initialises a "custom" jDE logger.
+var jDELogger = log.New(os.Stderr, " *** δ jDE:", log.Ldate|log.Ltime|log.Lshortfile)
+
+// Init initialises the jDE algorithm, performs sanity checks on the inputs.
+func (j *JDE) Init(generations, benchMinIters, mutStrategy, adptScheme, np int, f, cr float64, dimensions []int, bench string, ch chan []stats.Stats, chAlgoMeans chan *stats.AlgoBenchMean) {
+	if j == nil {
+		jDELogger.Fatalln("jDE needs to be initialised before calling RunjDE, exiting...")
+	}
+
+	// check input parameters.
+	switch {
+	case generations == 0:
+		jDELogger.Fatalln("Generations cannot be 0, got", generations)
+
+	case generations == -1:
+		jDELogger.Println("Generations is '-1', disabling generation limits..")
+
+	case benchMinIters < 1:
+		jDELogger.Fatalln("Minimum bench iterations cannot be less than 1, got:", benchMinIters)
+
+	case mutStrategy < 0 || mutStrategy > 17:
+		jDELogger.Fatalln("Mutation strategy needs to be from the interval <0; 17>, got", mutStrategy)
+
+	case adptScheme < 0 || adptScheme > 1:
+		jDELogger.Fatalln("Parameter self-adaptation scheme needs to be from the interval <0; 1>, got", adptScheme)
+
+	case np < jDEMinNP:
+		jDELogger.Fatalf("NP cannot be less than %d, got: %d\n.", jDEMinNP, np)
+
+	case f < fMin || f > fMax:
+		jDELogger.Fatalf("F needs to be from the interval <%f;%f>, got: %f\n.", fMin, fMax, f)
+
+	case cr < crMin || cr > crMax:
+		jDELogger.Fatalf("CR needs to be from the interval <%f;>%f, got: %f\n.", crMin, crMax, cr)
+
+	case len(dimensions) == 0:
+		jDELogger.Fatalf("Dimensions cannot be empty, got: %+v\n", dimensions)
+
+	case bench == "":
+		jDELogger.Fatalln("Bench cannot be empty, got:", bench)
+	}
+
+	j.Generations = generations
+	j.BenchMinIters = benchMinIters
+	j.MutationStrategy = mutStrategy
+	j.AdptScheme = adptScheme
+	j.NP = np
+	j.F = f
+	j.CR = cr
+	j.Dimensions = dimensions
+	j.BenchName = bench
+	j.ch = ch
+	j.chAlgoMeans = chAlgoMeans
+
+	j.initialised = true
+}
+
+// InitAndRun initialises the jDE algorithm, performs sanity checks on the
+// inputs and calls the Run method.
+func (j *JDE) InitAndRun(generations, benchMinIters, mutStrategy, adptScheme, np int, f, cr float64, dimensions []int, bench string, ch chan []stats.Stats, chAlgoMeans chan *stats.AlgoBenchMean) {
+	if j == nil {
+		jDELogger.Fatalln("jDE is nil, NewjDE() needs to be called first. exiting...")
+	}
+
+	j.Init(generations, benchMinIters, mutStrategy, adptScheme, np, f, cr, dimensions, bench, ch, chAlgoMeans)
+
+	j.Run()
+}
+
+// Run self-adapting differential evolution algorithm.
+func (j *JDE) Run() {
+	if j == nil {
+		jDELogger.Fatalln("jDE is nil, NewjDE() needs to be called first. exiting...")
+	}
+
+	if !j.initialised {
+		jDELogger.Fatalln("jDE needs to be initialised before calling Run(), exiting...")
+	}
+
+	var jDEStats []stats.Stats
+
+	jDEMeans := &stats.AlgoBenchMean{
+		Algo:       "jDE",
+		BenchMeans: make([]stats.BenchMean, 0, len(j.Dimensions)),
+	}
+
+	// run evolve for for all dimensions.
+	for _, dim := range j.Dimensions {
+		maxFES := bench.GetGAMaxFES(dim)
+		fesPerIter := int(float64(maxFES / j.NP))
+		jDEStatDimX := &stats.Stats{
+			Algo:        "jDE",
+			Dimens:      dim,
+			Iterations:  j.BenchMinIters,
+			Generations: maxFES,
+			NP:          j.NP,
+			F:           j.F,
+			CR:          j.CR,
+		}
+		funcStats := &stats.FuncStats{BenchName: j.BenchName}
+		dimXMean := &stats.BenchMean{
+			Bench:       j.BenchName,
+			Dimens:      dim,
+			Iterations:  j.BenchMinIters,
+			Generations: maxFES,
+			Neighbours:  -1,
+		}
+		benchFuncParams := bench.FunctionParams[j.BenchName]
+		uniDist := distuv.Uniform{
+			Min: benchFuncParams.Min(),
+			Max: benchFuncParams.Max(),
+		}
+
+		// jDELogger.Printf("running bench \"%s\" for %dD, maxFES: %d\n",
+		// j.BenchName, dim, maxFES,
+		// )
+
+		funcStats.BenchResults = make([]stats.BenchRound, j.BenchMinIters)
+
+		// rand.Seed(uint64(time.Now().UnixNano()))
+		// create and seed a source of preudo-randomness
+		src := rand.NewSource(uint64(rand.Int63()))
+
+		// track execution duration.
+		start := time.Now()
+
+		for iter := 0; iter < j.BenchMinIters; iter++ {
+			jDELogger.Printf("run: %d, bench: %s, %dD, started at %s",
+				iter, j.BenchName, dim, start,
+			)
+
+			funcStats.BenchResults[iter].Iteration = iter
+
+			uniDist.Src = src
+
+			// create a population with known params.
+			pop := newPopulation(j.BenchName, j.NP, dim)
+
+			// set population seed.
+			pop.Seed = uint64(time.Now().UnixNano())
+			// initialise the population.
+			pop.Init()
+			// jDELogger.Printf("%+v\n", pop.Population)
+
+			var bestResult float64
+
+			// the core.
+			for i := 0; i < fesPerIter; i++ {
+				r := j.evaluate(pop)
+
+				// distinguish the first or any of the subsequent iterations.
+				switch i {
+				case 0:
+					bestResult = r
+
+				default:
+					// call evolve where jDE runs.
+					j.evolve(pop, &uniDist)
+
+					// take measurements of current population fitness.
+					r = j.evaluate(pop)
+
+					// save if better.
+					if r < bestResult {
+						bestResult = r
+					}
+				}
+
+				funcStats.BenchResults[iter].Results = append(
+					funcStats.BenchResults[iter].Results,
+					bestResult,
+				)
+				// this block makes sure we properly count func evaluations for
+				// the purpose of correctly comparable plot comparison. i.e.
+				// append the winning (current best) value NP-1 (the first best
+				// is already saved at this point) times to represent the fact
+				// that while evaluating (and comparing) other population
+				// individuals to the current best value is taking place in the
+				// background, the current best value itself is kept around and
+				// symbolically saved as the best of the Generation.
+				for x := 0; x < j.NP-1; x++ {
+					funcStats.BenchResults[iter].Results = append(
+						funcStats.BenchResults[iter].Results,
+						bestResult,
+					)
+				}
+			}
+		}
+
+		elapsed := time.Since(start)
+
+		jDELogger.Printf("completed: bench: %s, %dD, computing took %s\n",
+			j.BenchName, dim, elapsed,
+		)
+
+		// get mean vals.
+		dimXMean.MeanVals = stats.GetMeanVals(funcStats.BenchResults, maxFES)
+		funcStats.MeanVals = dimXMean.MeanVals
+
+		jDEStatDimX.BenchFuncStats = append(
+			jDEStatDimX.BenchFuncStats, *funcStats,
+		)
+
+		jDEStats = append(jDEStats, *jDEStatDimX)
+
+		jDEMeans.BenchMeans = append(jDEMeans.BenchMeans, *dimXMean)
+	}
+
+	sort.Sort(jDEMeans)
+
+	j.chAlgoMeans <- jDEMeans
+	j.ch <- jDEStats
+}
+
+// evaluate evaluates the fitness of current population.
+func (j *JDE) evaluate(pop *Population) float64 {
+	f := bench.Functions[pop.Problem]
+
+	bestIndividual := pop.Population[pop.GetBestIdx()].CurX
+	bestSolution := f(bestIndividual)
+
+	return bestSolution
+}
+
+// evolve evolves a population by running the jDE (self-adapting Differential
+// Evolution) algorithm on the passed population until termination conditions
+// are met.
+// nolint: gocognit
+func (j *JDE) evolve(pop *Population, uniDist *distuv.Uniform) {
+	popCount := len(pop.Population)
+
+	for i, currentIndividual := range pop.Population {
+		idcs := make([]int, 0, popCount-1)
+
+		// gather indices.
+		for k := 0; k < popCount; k++ {
+			if k != i {
+				idcs = append(idcs, k)
+			}
+		}
+
+		// randomly choose 3 of those idcs.
+		selectedIdcs := make([]int, 0)
+
+		selectedA := false
+		selectedB := false
+		selectedC := false
+
+		for !selectedA {
+			candidateA := rand.Intn(len(idcs)) % len(idcs)
+
+			if candidateA != i {
+				selectedIdcs = append(selectedIdcs, candidateA)
+				selectedA = true
+			}
+		}
+
+		for !selectedB {
+			a := selectedIdcs[0]
+			candidateB := rand.Intn(len(idcs)) % len(idcs)
+
+			if candidateB != i && candidateB != a {
+				selectedIdcs = append(selectedIdcs, candidateB)
+				selectedB = true
+			}
+		}
+
+		for !selectedC {
+			a := selectedIdcs[0]
+			b := selectedIdcs[1]
+			candidateC := rand.Intn(len(idcs)) % len(idcs)
+
+			if candidateC != i && candidateC != a && candidateC != b {
+				selectedIdcs = append(selectedIdcs, candidateC)
+				selectedC = true
+			}
+		}
+
+		// selected contains the selected population individuals.
+		selected := make([]PopulationIndividual, 0)
+
+		// select individuals for rand/1/bin
+		for _, idx := range selectedIdcs {
+			for k := 0; k < popCount; k++ {
+				if k == idx {
+					selected = append(selected, pop.Population[idx])
+				}
+			}
+		}
+
+		mutant := make([]float64, pop.Dimen)
+
+		// mutate.
+		for k := 0; k < pop.Dimen; k++ {
+			// mutant = a + mut * (b - c)
+			mutant[k] = selected[0].CurX[k] + (j.F * (selected[1].CurX[k] - selected[2].CurX[k]))
+
+			// clip values to <0;1>.
+			switch {
+			case mutant[k] < 0:
+				mutant[k] = 0
+
+			case mutant[k] > 1:
+				mutant[k] = 1
+			}
+		}
+
+		crossPoints := make([]bool, pop.Dimen)
+
+		// prepare crossover points (binomial crossover).
+		for k := 0; k < pop.Dimen; k++ {
+			if v := uniDist.Rand(); v < j.CR {
+				crossPoints[k] = true
+			} else {
+				crossPoints[k] = false
+			}
+		}
+
+		trial := make([]float64, pop.Dimen)
+
+		// recombine using crossover points.
+		for k := 0; k < pop.Dimen; k++ {
+			if crossPoints[k] {
+				trial[k] = currentIndividual.CurX[k]
+			}
+		}
+
+		f := bench.Functions[pop.Problem]
+		currentFitness := f(currentIndividual.CurX)
+		trialFitness := f(trial)
+
+		// replace if better.
+		if trialFitness < currentFitness {
+			pop.Population[i].CurX = trial
+		}
+	}
+}
+
+// NewjDE returns a pointer to a new, uninitialised jDE instance.
+func NewjDE() *JDE {
+	return &JDE{}
+}
+
+// LogPrintln wraps the jDE logger's Println func.
+func LogPrintln(v ...any) {
+	jDELogger.Println(v...)
+}
+
+// LogPrintf wraps the jDE logger's Printf func.
+func LogPrintf(s string, v ...any) {
+	jDELogger.Printf(s, v...)
+}
+
+// LogFatalln wraps the jDE logger's Fatalln func.
+func LogFatalln(s string) {
+	jDELogger.Fatalln(s)
+}
+
+// LogFatalf wraps the jDE logger's Fatalf func.
+func LogFatalf(s string, v ...any) {
+	jDELogger.Fatalf(s, v...)
+}

algo/de/mutationStrategies.go

@@ -0,0 +1,51 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package de
+
+const (
+	// DEBest1Exp is the DE/best/1/exp strategy.
+	DEBest1Exp int = iota
+	// DERand1Exp is the DE/rand/1/exp strategy.
+	DERand1Exp
+	// DERandtoBest1Exp is the DE/rand-to-best/1/exp strategy.
+	DERandtoBest1Exp
+	// DEBest2Exp is the DE/best/2/exp strategy.
+	DEBest2Exp
+	// DERand2Exp is the DE/rand/2/exp strategy.
+	DERand2Exp
+
+	// binomial cross-over strategies.
+
+	// DEBest1Bin is the DE/best/1/bin strategy.
+	DEBest1Bin
+	// DERand1Bin is the DE/rand/1/bin strategy.
+	DERand1Bin
+	// DERandtoBest1Bin is the DE/rand-to-best/1/bin strategy.
+	DERandtoBest1Bin
+	// DEBest2Bin is the DE/best/2/bin strategy.
+	DEBest2Bin
+	// DERand2Bin is the DE/rand/2/bin strategy.
+	DERand2Bin
+
+	// 3-wide changes.
+
+	// DEBest3Exp is the DE/best/3/exp strategy.
+	DEBest3Exp
+	// DEBest3Bin is the DE/best/3/bin strategy.
+	DEBest3Bin
+	// DERand3Exp is the DE/rand/3/exp strategy.
+	DERand3Exp
+	// DERand3Bin is the DE/rand/3/bin strategy.
+	DERand3Bin
+
+	// DERandtoCurrent2E is the DE/rand-to-current/2/exp strategy.
+	DERandtoCurrent2Exp
+	// DERandtoCurrent2Bin is the DE/rand-to-current/2/bin strategy.
+	DERandtoCurrent2Bin
+
+	// DERandtoBestandCurrent2Exp is the DE/rand-to-best-and-current/2/exp strategy.
+	DERandtoBestandCurrent2Exp
+	// DERandtoBestandCurrent2Bin is the DE/rand-to-best-and-current/2/bin strategy.
+	DERandtoBestandCurrent2Bin
+)

algo/de/population.go

@@ -0,0 +1,161 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package de
+
+import (
+	"git.dotya.ml/wanderer/math-optim/bench"
+	"golang.org/x/exp/rand"
+
+	"gonum.org/v1/gonum/stat/distuv"
+)
+
+type (
+	// DecisionVector is a []float64 abstraction representing the decision vector.
+	DecisionVector []float64
+	// FitnessVector is a []float64 abstraction representing the fitness vector.
+	FitnessVector []float64
+	// ConstraintVector is a []float64 abstraction representing the constraint vector.
+	ConstraintVector []float64
+)
+
+// PopulationIndividual representats a single population individual.
+type PopulationIndividual struct {
+	CurX DecisionVector
+	CurV DecisionVector
+	CurC ConstraintVector
+	CurF FitnessVector
+
+	BestX DecisionVector
+	BestC ConstraintVector
+	BestF FitnessVector
+}
+
+// ChampionIndividual is a representation of the best individual currently
+// available in the population.
+type ChampionIndividual struct {
+	X DecisionVector
+	C ConstraintVector
+	F FitnessVector
+}
+
+// Population groups population individuals (agents) with metadata about the population.
+type Population struct {
+	// Population is a slice of population individuals.
+	Population []PopulationIndividual
+	// Problem is the current benchmarking function this population is attempting to optimise.
+	Problem string
+	// Dimen is the dimensionality of the problem being optimised.
+	Dimen int
+	// Seed is the value used to (re)init population.
+	Seed uint64
+}
+
+// GetIndividal returns a reference to individual at position n.
+func (p *Population) GetIndividual(n uint) *PopulationIndividual { return &PopulationIndividual{} }
+
+// GetBestIdx returns the index of the best population individual.
+func (p *Population) GetBestIdx() int {
+	f := bench.Functions[p.Problem]
+
+	bestIndividual := 0
+	// the first one is the best one.
+	bestVal := f(p.Population[0].CurX)
+
+	for i, v := range p.Population {
+		current := f(v.CurX)
+
+		if current < bestVal {
+			bestIndividual = i
+		}
+	}
+
+	return bestIndividual
+}
+
+// GetWorstIdx returns the index of the worst population individual.
+func (p *Population) GetWorstIdx() int {
+	f := bench.Functions[p.Problem]
+
+	worstIndividual := 0
+	// the first one is the worst one.
+	worstVal := f(p.Population[0].CurX)
+
+	for i, v := range p.Population {
+		current := f(v.CurX)
+
+		if current > worstVal {
+			worstIndividual = i
+		}
+	}
+
+	return worstIndividual
+}
+
+func (p *Population) SetX(n int, nuX DecisionVector) {}
+func (p *Population) SetV(n int, nuV DecisionVector) {}
+
+// Init initialises all individuals to random values.
+func (p *Population) Init() {
+	benchFuncParams := bench.FunctionParams[p.Problem]
+
+	uniform := distuv.Uniform{
+		Min: benchFuncParams.Min(),
+		Max: benchFuncParams.Max(),
+	}
+	uniform.Src = rand.NewSource(p.Seed)
+
+	// jDELogger.Printf("population initialisation - popCount: %d, seed: %d\n",
+	// len(p.Population), p.Seed,
+	// )
+
+	for i, v := range p.Population {
+		v.CurX = make([]float64, p.Dimen)
+
+		for j := 0; j < p.Dimen; j++ {
+			v.CurX[j] = uniform.Rand()
+		}
+
+		p.Population[i] = v
+	}
+}
+
+// Reinit reinitialises all individuals.
+func (p *Population) Reinit() {
+	p.Init()
+}
+
+// ReinitN reinitialises the individual at position n.
+func (p *Population) ReinitN(n uint) {}
+
+// Clear sets all vectors to 0.
+func (p *Population) Clear() {
+	if p.Population != nil {
+		for _, v := range p.Population {
+			v.CurX = make([]float64, p.Dimen)
+			v.CurC = make([]float64, p.Dimen)
+			v.CurF = make([]float64, p.Dimen)
+			v.CurV = make([]float64, p.Dimen)
+			v.BestX = make([]float64, p.Dimen)
+			v.BestC = make([]float64, p.Dimen)
+			v.BestF = make([]float64, p.Dimen)
+		}
+	}
+}
+
+// meanVelocity computes the mean current velocity of all individuals in the population.
+func (p *Population) MeanVelocity() float64 { return 0.0 }
+
+// newPopulation returns a pointer to a new, uninitialised population.
+func newPopulation(benchProblem string, np, dimen int) *Population {
+	p := &Population{}
+
+	p.Problem = benchProblem
+
+	p.Dimen = dimen
+
+	// pre-alloc.
+	p.Population = make([]PopulationIndividual, np)
+
+	return p
+}

algo/ga/doc.go

@@ -0,0 +1,5 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+// Package ga implements Genetic Algorithms.
+package ga

algo/ga/error.go

@@ -0,0 +1,28 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package ga
+
+type gaError struct {
+	s string
+}
+
+var (
+	errGenerationsIsZero    = newGAError("Generations cannot be 0")
+	errBenchMinItersIsLTOne = newGAError("Minimum bench iterations cannot be less than 1")
+	errNPIsZero             = newGAError("NP cannot be 0")
+	errKIsZero              = newGAError("K cannot be 0")
+	errMIsZero              = newGAError("M cannot be 0")
+	errNIsZero              = newGAError("N cannot be 0")
+	errNjumpsIsZero         = newGAError("Njumps cannot be 0")
+	errDimensionsUnset      = newGAError("Dimensions cannot be unset")
+	errBenchUnset           = newGAError("Bench cannot be unset")
+)
+
+func (e *gaError) Error() string {
+	return e.s
+}
+
+func newGAError(text string) error {
+	return &gaError{s: text}
+}

algo/ga/jde.go

@@ -0,0 +1,427 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package ga
+
+import (
+	"sort"
+	"time"
+
+	"golang.org/x/exp/rand"
+
+	"git.dotya.ml/wanderer/math-optim/bench/cec2020"
+	"git.dotya.ml/wanderer/math-optim/stats"
+	"gonum.org/v1/gonum/stat/distuv"
+)
+
+// JDE is a holder for the settings of an instance of a self-adapting
+// differential evolution (jDE) algorithm.
+type JDE struct {
+	// Generations denotes the number of generations the population evolves
+	// for. Special value -1 disables limiting the number of generations.
+	Generations int
+	// BenchMinIters is the number of iterations the bench function will be re-run (for statistical purposes).
+	BenchMinIters int
+	// Dimensions to solve the problem for.
+	Dimensions []int
+	// F is the initial value of the differential weight (mutation/weighting factor).
+	F float64
+	// CR is the initial value of the crossover probability constant.
+	CR float64
+	// rngF is a random number generator for differential weight (mutation/weighting factor).
+	rngF distuv.Uniform
+	// cr is a random number generator for crossover probability constant adapted over time.
+	rngCR distuv.Uniform
+	// MutationStrategy selects the mutation strategy, i.e. the variant of the
+	// jDE algorithm (0..17), see mutationStrategies.go for more details.
+	MutationStrategy int
+	// AdptScheme is the parameter self-adaptation scheme (0..1).
+	AdptScheme int
+	// NP is the initial population size.
+	NP int
+	// BenchName is a name of the problem to optimise.
+	BenchName string
+	// ch is a channel for writing back computed results.
+	ch chan []stats.Stats
+	// chAlgoMeans is a channel for writing back algo means.
+	chAlgoMeans chan *stats.AlgoBenchMean
+	// initialised denotes the initialisation state of the struct.
+	initialised bool
+}
+
+const (
+	// jDEMinNP is the minimum size of the initial population for jDE.
+	// for jDE PaGMO specifies 8.
+	jDEMinNP = 4
+	// fMin is the minimum allowed value of the differential weight.
+	fMin = 0.36
+	// fMax is the maximum allowed value of the differential weight.
+	fMax = 1.0
+	// crMin is the minimum allowed value of the crossover probability constant.
+	crMin = 0.0
+	// crMax is the maximum allowed value of the crossover probability constant.
+	crMax = 1.0
+
+	// tau1 is used in parameter self-adaptation.
+	tau1 = 0.1
+	// tau2 is used in parameter self-adaptation.
+	tau2 = 0.1
+	// fl is used in parameter self-adaptation.
+	fl = 0.1
+	// fu is used in parameter self-adaptation.
+	fu = 0.9
+)
+
+// dELogger is a "custom" jDE logger.
+var jDELogger = newLogger(" *** δ jDE:")
+
+// Init initialises the jDE algorithm, performs sanity checks on the inputs.
+func (j *JDE) Init(generations, benchMinIters, mutStrategy, adptScheme, np int, f, cr float64, dimensions []int, bench string, ch chan []stats.Stats, chAlgoMeans chan *stats.AlgoBenchMean) {
+	if j == nil {
+		jDELogger.Fatalln("jDE needs to be initialised before calling Run(), exiting...")
+	}
+
+	// check input parameters.
+	switch {
+	case generations == 0:
+		jDELogger.Fatalln("Generations cannot be 0, got", generations)
+
+	case generations == -1:
+		jDELogger.Println("Generations is '-1', disabling generation limits..")
+
+	case benchMinIters < 1:
+		jDELogger.Fatalln("Minimum bench iterations cannot be less than 1, got:", benchMinIters)
+
+	case mutStrategy < 0 || mutStrategy > 17:
+		jDELogger.Fatalln("Mutation strategy needs to be from the interval <0; 17>, got", mutStrategy)
+
+	case adptScheme < 0 || adptScheme > 1:
+		jDELogger.Fatalln("Parameter self-adaptation scheme needs to be from the interval <0; 1>, got", adptScheme)
+
+	case np < jDEMinNP:
+		jDELogger.Fatalf("NP cannot be less than %d, got: %d\n.", jDEMinNP, np)
+
+	case f < fMin || f > fMax:
+		jDELogger.Fatalf("F needs to be from the interval <%f;%f>, got: %f\n.", fMin, fMax, f)
+
+	case cr < crMin || cr > crMax:
+		jDELogger.Fatalf("CR needs to be from the interval <%f;>%f, got: %f\n.", crMin, crMax, cr)
+
+	case len(dimensions) == 0:
+		jDELogger.Fatalf("Dimensions cannot be empty, got: %+v\n", dimensions)
+
+	case bench == "":
+		jDELogger.Fatalln("Bench cannot be empty, got:", bench)
+	}
+
+	j.Generations = generations
+	j.BenchMinIters = benchMinIters
+	j.MutationStrategy = mutStrategy
+	j.AdptScheme = adptScheme
+	j.NP = np
+	j.F = f
+	j.CR = cr
+
+	rngsrc := rand.NewSource(uint64(rand.Int63()))
+
+	j.rngF = distuv.Uniform{Min: fMin, Max: fMax, Src: rngsrc}
+	j.rngCR = distuv.Uniform{Min: crMin, Max: crMax, Src: rngsrc}
+
+	j.Dimensions = dimensions
+	j.BenchName = bench
+	j.ch = ch
+	j.chAlgoMeans = chAlgoMeans
+
+	j.initialised = true
+
+	gaLogger.Printf("jDE init done for bench %s", j.BenchName)
+}
+
+// InitAndRun initialises the jDE algorithm, performs sanity checks on the
+// inputs and calls the Run method.
+func (j *JDE) InitAndRun(generations, benchMinIters, mutStrategy, adptScheme, np int, f, cr float64, dimensions []int, bench string, ch chan []stats.Stats, chAlgoMeans chan *stats.AlgoBenchMean) {
+	if j == nil {
+		jDELogger.Fatalln("jDE is nil, NewjDE() needs to be called first. exiting...")
+	}
+
+	j.Init(generations, benchMinIters, mutStrategy, adptScheme, np, f, cr, dimensions, bench, ch, chAlgoMeans)
+
+	j.Run()
+}
+
+// Run self-adapting differential evolution algorithm.
+func (j *JDE) Run() {
+	if j == nil {
+		jDELogger.Fatalln("jDE is nil, NewjDE() needs to be called first. exiting...")
+	}
+
+	if !j.initialised {
+		jDELogger.Fatalln("jDE needs to be initialised before calling Run(), exiting...")
+	}
+
+	var jDEStats []stats.Stats
+
+	jDEMeans := &stats.AlgoBenchMean{
+		Algo:       "jDE",
+		BenchMeans: make([]stats.BenchMean, 0, len(j.Dimensions)),
+	}
+
+	// run evolve for all dimensions.
+	for _, dim := range j.Dimensions {
+		maxFES := cec2020.GetMaxFES(dim)
+		fesPerIter := int(float64(maxFES / j.NP))
+		jDEStatDimX := &stats.Stats{
+			Algo:        "jDE",
+			Dimens:      dim,
+			Iterations:  j.BenchMinIters,
+			Generations: maxFES,
+			NP:          j.NP,
+			F:           j.F,
+			CR:          j.CR,
+		}
+		funcStats := &stats.FuncStats{BenchName: j.BenchName}
+		dimXMean := &stats.BenchMean{
+			Bench:       j.BenchName,
+			Dimens:      dim,
+			Iterations:  j.BenchMinIters,
+			Generations: maxFES,
+			Neighbours:  -1,
+		}
+		uniDist := distuv.Uniform{
+			Min: cec2020.SearchRange.Min(),
+			Max: cec2020.SearchRange.Max(),
+		}
+
+		if maxFES == -1 {
+			jDELogger.Fatalf("could not get maxFES for current dim (%d), bailing", dim)
+		}
+
+		jDELogger.Printf("running bench \"%s\" for %dD, maxFES: %d\n",
+			j.BenchName, dim, maxFES,
+		)
+
+		funcStats.BenchResults = make([]stats.BenchRound, j.BenchMinIters)
+
+		// rand.Seed(uint64(time.Now().UnixNano()))
+		// create and seed a source of pseudo-randomness
+		src := rand.NewSource(uint64(rand.Int63()))
+
+		// track execution duration.
+		start := time.Now()
+
+		for iter := 0; iter < j.BenchMinIters; iter++ {
+			jDELogger.Printf("run: %d, bench: %s, %dD, started at %s",
+				iter, j.BenchName, dim, start,
+			)
+
+			funcStats.BenchResults[iter].Iteration = iter
+
+			uniDist.Src = src
+			// uniDist.Src = rand.NewSource(uint64(rand.Int63()))
+
+			// create a population with known params.
+			pop := newPopulation(j.BenchName, j.NP, dim)
+
+			// set population seed.
+			pop.Seed = uint64(time.Now().UnixNano())
+			// initialise the population.
+			pop.Init()
+
+			var bestResult float64
+
+			// the core.
+			for i := 0; i < fesPerIter; i++ {
+				r := j.evaluate(pop)
+
+				// distinguish the first or any of the subsequent iterations.
+				switch i {
+				case 0:
+					bestResult = r
+
+				default:
+					// call evolve where jDE runs.
+					j.evolve(pop, &uniDist)
+
+					// take measurements of current population fitness.
+					r = j.evaluate(pop)
+
+					// save if better.
+					if r < bestResult {
+						bestResult = r
+					}
+				}
+
+				funcStats.BenchResults[iter].Results = append(
+					funcStats.BenchResults[iter].Results,
+					bestResult,
+				)
+				// this block makes sure we properly count func evaluations for
+				// the purpose of correctly comparable plot comparison. i.e.
+				// append the winning (current best) value NP-1 (the first best
+				// is already saved at this point) times to represent the fact
+				// that while evaluating (and comparing) other population
+				// individuals to the current best value is taking place in the
+				// background, the current best value itself is kept around and
+				// symbolically saved as the best of the Generation.
+				for x := 0; x < j.NP-1; x++ {
+					funcStats.BenchResults[iter].Results = append(
+						funcStats.BenchResults[iter].Results,
+						bestResult,
+					)
+				}
+			}
+		}
+
+		elapsed := time.Since(start)
+
+		jDELogger.Printf("completed: bench: %s, %dD, computing took %s\n",
+			j.BenchName, dim, elapsed,
+		)
+
+		// get mean vals.
+		dimXMean.MeanVals = stats.GetMeanVals(funcStats.BenchResults, maxFES)
+		funcStats.MeanVals = dimXMean.MeanVals
+
+		jDEStatDimX.BenchFuncStats = append(
+			jDEStatDimX.BenchFuncStats, *funcStats,
+		)
+
+		jDEStats = append(jDEStats, *jDEStatDimX)
+
+		jDEMeans.BenchMeans = append(jDEMeans.BenchMeans, *dimXMean)
+	}
+
+	sort.Sort(jDEMeans)
+
+	j.chAlgoMeans <- jDEMeans
+	j.ch <- jDEStats
+}
+
+// evaluate evaluates the fitness of current population.
+func (j *JDE) evaluate(pop *Population) float64 {
+	f := pop.ProblemFunc
+
+	bestIndividual := pop.Population[pop.GetBestIdx()].CurX
+	bestSolution := f(bestIndividual)
+
+	return bestSolution
+}
+
+// evolve evolves a population by running the jDE (self-adapting Differential
+// Evolution) algorithm on the passed population until termination conditions
+// are met.
+// nolint: gocognit
+func (j *JDE) evolve(pop *Population, uniDist *distuv.Uniform) {
+	f := pop.ProblemFunc
+	genChampIdx := pop.GetBestIdx()
+	genChamp := ChampionIndividual{
+		X: pop.Population[genChampIdx].CurX,
+	}
+	genChampFitness := f(genChamp.X)
+
+	for i, currentIndividual := range pop.Population {
+		currentFitness := f(currentIndividual.CurX)
+
+		if genChampFitness < f(pop.Champion.X) {
+			pop.Champion.X = genChamp.X
+		}
+
+		donors := pop.SelectDonors(i)
+
+		mutant := mutate(j.MutationStrategy, currentIndividual, genChamp, pop, donors...)
+
+		if mutant == nil {
+			jDELogger.Fatalln("Somehow you managed to pass in an out-of-bounds mutation strategy, don't know what to do, exiting...")
+		}
+
+		crossPoints := make([]bool, pop.Dimen)
+
+		// prepare crossover points (binomial crossover).
+		for k := 0; k < pop.Dimen; k++ {
+			if v := uniDist.Rand(); v < pop.BestCR {
+				crossPoints[k] = true
+			} else {
+				crossPoints[k] = false
+			}
+		}
+
+		trial := make([]float64, pop.Dimen)
+
+		// recombine using crossover points.
+		for k := 0; k < pop.Dimen; k++ {
+			if crossPoints[k] {
+				trial[k] = currentIndividual.CurX[k]
+			} else {
+				trial[k] = mutant[k]
+			}
+		}
+
+		trialFitness := f(trial)
+
+		// replace if better.
+		if trialFitness < currentFitness {
+			pop.Population[i].CurX = trial
+		}
+	}
+
+	// adapt parameters.
+	j.adaptParameters(pop)
+}
+
+// adptParameters adapts parameters F and CR based on
+// https://labraj.feri.um.si/images/0/05/CEC09_slides_Brest.pdf.
+func (j *JDE) adaptParameters(p *Population) {
+	var nuF float64
+
+	var nuCR float64
+
+	switch {
+	case j.AdptScheme == 0:
+		if rand2 := j.getRandF(); rand2 < tau1 {
+			nuF = fl + (j.getRandF() * fu)
+		} else {
+			nuF = p.f[len(p.f)-1]
+		}
+
+		if rand4 := j.getRandCR(); rand4 < tau2 {
+			nuCR = j.getRandCR()
+		} else {
+			nuCR = p.cr[len(p.cr)-1]
+		}
+
+	case j.AdptScheme == 1:
+		nuF = p.BestF + j.getRandF()*0.5
+		nuCR = p.BestCR + j.getRandCR()*0.5
+	}
+
+	// sort out F.
+	p.f = append(p.f, nuF)
+	p.CurF = nuF
+	// sort out CR
+	p.cr = append(p.cr, nuCR)
+	p.CurCR = nuCR
+
+	// update best, if improved.
+	switch {
+	case nuF < p.BestF:
+		p.BestF = nuF
+
+	case nuCR < p.BestCR:
+		p.BestCR = nuCR
+	}
+}
+
+// getRandF returns a random value of F for parameter self-adaptation.
+func (j *JDE) getRandF() float64 {
+	return j.rngF.Rand()
+}
+
+// getRandCR returns a random value of CR for parameter self-adaptation.
+func (j *JDE) getRandCR() float64 {
+	return j.rngCR.Rand()
+}
+
+// NewjDE returns a pointer to a new, uninitialised jDE instance.
+func NewjDE() *JDE {
+	return &JDE{}
+}

algo/ga/log.go

@@ -0,0 +1,17 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package ga
+
+import (
+	"log"
+	"os"
+)
+
+// gaLogger declares and initialises a "custom" ga logger.
+var gaLogger = log.New(os.Stderr, " *** Γ ga:", log.Ldate|log.Ltime|log.Lshortfile)
+
+// newLogger can be used to get a logger with a custom prefix.
+func newLogger(prefix string) *log.Logger {
+	return log.New(os.Stderr, prefix, log.Ldate|log.Ltime|log.Lshortfile)
+}

algo/ga/mutation.go

@@ -0,0 +1,86 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package ga
+
+// mutation strategies iota consts.
+
+const (
+	best1Exp = iota
+	rand1Exp
+	randtoBest1Exp
+)
+
+// mutations.
+
+func clipVals(mutant []float64) []float64 {
+	for i := range mutant {
+		switch {
+		case mutant[i] < 0:
+			mutant[i] = 0
+
+		case mutant[i] > 1:
+			mutant[i] = 1
+		}
+	}
+
+	return mutant
+}
+
+// mutate performs the mutation of choice and returns the mutant.
+func mutate(mutStrategy int, currentIndividual PopulationIndividual, genChamp ChampionIndividual, pop *Population, donors ...PopulationIndividual) []float64 {
+	var mutant []float64
+
+	dim := pop.Dimen
+	curF := pop.CurF
+	champ := genChamp.X
+	curX := currentIndividual.CurX
+
+	switch mutStrategy {
+	case best1Exp:
+		mutant = mutBest1Exp(dim, curF, champ, donors...)
+
+	case rand1Exp:
+		mutant = mutRand1Exp(dim, curF, donors...)
+
+	case randtoBest1Exp:
+		mutant = mutRandtoBest1Exp(dim, curF, champ, curX, donors...)
+	default:
+		return nil
+	}
+
+	// return mutant with values clipped to <0;1>.
+	return clipVals(mutant)
+}
+
+func mutBest1Exp(dim int, curF float64, champ []float64, donors ...PopulationIndividual) []float64 {
+	mutant := make([]float64, dim)
+
+	for i := 0; i < dim; i++ {
+		// mutant[i] = curF + (bestF*(donors[1].CurX[i]) - donors[2].CurX[i])
+		mutant[i] = champ[i] + (curF*(donors[1].CurX[i]) - donors[2].CurX[i])
+	}
+
+	return mutant
+}
+
+func mutRand1Exp(dim int, curF float64, donors ...PopulationIndividual) []float64 {
+	mutant := make([]float64, dim)
+
+	for i := 0; i < dim; i++ {
+		// tmp[n] = popold[r1][n] + curF*(popold[r2][n]-popold[r3][n]);
+		mutant[i] = donors[0].CurX[i] + curF*(donors[1].CurX[i]-donors[2].CurX[i])
+	}
+
+	return mutant
+}
+
+func mutRandtoBest1Exp(dim int, curF float64, champ, currentIndividual []float64, donors ...PopulationIndividual) []float64 {
+	mutant := make([]float64, dim)
+
+	for i := 0; i < dim; i++ {
+		mutant[i] += currentIndividual[i] + (curF*champ[i] - currentIndividual[i]) + (curF * (donors[0].CurX[i] - donors[1].CurX[i]))
+	}
+
+	return mutant
+}

algo/ga/population.go

@@ -0,0 +1,241 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package ga
+
+import (
+	"git.dotya.ml/wanderer/math-optim/bench/cec2020"
+	"golang.org/x/exp/rand"
+
+	"gonum.org/v1/gonum/stat/distuv"
+)
+
+type (
+	// DecisionVector is a []float64 abstraction representing the decision vector.
+	DecisionVector []float64
+	// FitnessVector is a []float64 abstraction representing the fitness vector.
+	FitnessVector []float64
+	// ConstraintVector is a []float64 abstraction representing the constraint vector.
+	ConstraintVector []float64
+)
+
+// PopulationIndividual represents a single population individual.
+type PopulationIndividual struct {
+	CurX DecisionVector
+	CurF FitnessVector
+
+	BestX DecisionVector
+	BestC ConstraintVector
+	BestF FitnessVector
+}
+
+// ChampionIndividual is a representation of the best individual currently
+// available in the population.
+type ChampionIndividual struct {
+	X DecisionVector
+	// CR float64
+	// F  float64
+}
+
+// Population groups population individuals (agents) with metadata about the population.
+type Population struct {
+	// Population is a slice of population individuals.
+	Population []PopulationIndividual
+	// Champion represents the best individual of the population.
+	Champion ChampionIndividual
+	// Problem is the current benchmarking function this population is attempting to optimise.
+	Problem string
+	// ProblemFunction is the actual function to optimise.
+	ProblemFunc func([]float64) float64
+	// Dimen is the dimensionality of the problem being optimised.
+	Dimen int
+	// Seed is the value used to (re)init population.
+	Seed uint64
+
+	// f is the differential weight (mutation/weighting factor) adapted over time.
+	f []float64
+	// cr is the crossover probability constant adapted over time.
+	cr []float64
+	// BestF is the best recorded value of the differential weight F.
+	BestF float64
+	// BestCR is the best recorded value of the differential weight CR.
+	BestCR float64
+	// CurF is the current value of F.
+	CurF float64
+	// CurCR is the current value of the differential weight CR.
+	CurCR float64
+}
+
+// GetIndividual returns a reference to individual at position n.
+func (p *Population) GetIndividual(n uint) *PopulationIndividual { return &PopulationIndividual{} }
+
+// GetBestIdx returns the index of the best population individual.
+func (p *Population) GetBestIdx() int {
+	f := p.ProblemFunc
+
+	bestIndividual := 0
+	// the first one is the best one.
+	bestVal := f(p.Population[0].CurX)
+
+	for i, v := range p.Population {
+		current := f(v.CurX)
+
+		if current < bestVal {
+			bestIndividual = i
+		}
+	}
+
+	return bestIndividual
+}
+
+// GetWorstIdx returns the index of the worst population individual.
+func (p *Population) GetWorstIdx() int {
+	f := p.ProblemFunc
+
+	worstIndividual := 0
+	// the first one is the worst one.
+	worstVal := f(p.Population[0].CurX)
+
+	for i, v := range p.Population {
+		current := f(v.CurX)
+
+		if current > worstVal {
+			worstIndividual = i
+		}
+	}
+
+	return worstIndividual
+}
+
+// Init initialises all individuals to random values.
+func (p *Population) Init() {
+	uniform := distuv.Uniform{
+		Min: cec2020.SearchRange.Min(),
+		Max: cec2020.SearchRange.Max(),
+	}
+	uniform.Src = rand.NewSource(p.Seed)
+
+	// gaLogger.Printf("population initialisation - popCount: %d, seed: %d\n",
+	// len(p.Population), p.Seed,
+	// )
+
+	for i, v := range p.Population {
+		v.CurX = make([]float64, p.Dimen)
+
+		for j := 0; j < p.Dimen; j++ {
+			v.CurX[j] = uniform.Rand()
+		}
+
+		p.Population[i] = v
+	}
+
+	p.f = make([]float64, p.Size())
+	p.cr = make([]float64, p.Size())
+
+	p.Champion = ChampionIndividual{X: p.Population[p.GetBestIdx()].CurX}
+}
+
+// Reinit reinitialises all individuals.
+func (p *Population) Reinit() {
+	p.Init()
+}
+
+// ReinitN reinitialises the individual at position n.
+func (p *Population) ReinitN(n uint) {}
+
+// Clear sets all vectors to 0.
+func (p *Population) Clear() {
+	if p.Population != nil {
+		for _, v := range p.Population {
+			v.CurX = make([]float64, p.Dimen)
+			v.CurF = make([]float64, p.Dimen)
+			v.BestX = make([]float64, p.Dimen)
+			v.BestC = make([]float64, p.Dimen)
+			v.BestF = make([]float64, p.Dimen)
+		}
+	}
+}
+
+func (p *Population) SelectDonors(currentIdx int) []PopulationIndividual {
+	popCount := p.Size()
+
+	idcs := make([]int, 0, popCount-1)
+
+	// gather indices.
+	for k := 0; k < popCount; k++ {
+		if k != currentIdx {
+			idcs = append(idcs, k)
+		}
+	}
+
+	// randomly choose 3 of those idcs.
+	selectedIdcs := make([]int, 0)
+
+	selectedA := false
+	selectedB := false
+	selectedC := false
+
+	for !selectedA {
+		candidateA := rand.Intn(len(idcs)) % len(idcs)
+
+		if candidateA != currentIdx {
+			selectedIdcs = append(selectedIdcs, candidateA)
+			selectedA = true
+		}
+	}
+
+	for !selectedB {
+		a := selectedIdcs[0]
+		candidateB := rand.Intn(len(idcs)) % len(idcs)
+
+		if candidateB != currentIdx && candidateB != a {
+			selectedIdcs = append(selectedIdcs, candidateB)
+			selectedB = true
+		}
+	}
+
+	for !selectedC {
+		a := selectedIdcs[0]
+		b := selectedIdcs[1]
+		candidateC := rand.Intn(len(idcs)) % len(idcs)
+
+		if candidateC != currentIdx && candidateC != a && candidateC != b {
+			selectedIdcs = append(selectedIdcs, candidateC)
+			selectedC = true
+		}
+	}
+
+	// selected contains the selected population individuals.
+	selected := make([]PopulationIndividual, 0)
+
+	// select individuals for donation.
+	for _, idx := range selectedIdcs {
+		for k := 0; k < popCount; k++ {
+			if k == idx {
+				selected = append(selected, p.Population[idx])
+			}
+		}
+	}
+
+	return selected
+}
+
+// MeanVelocity computes the mean current velocity of all individuals in the population.
+func (p *Population) MeanVelocity() float64 { return 0.0 }
+
+// Size returns the number of population individuals.
+func (p *Population) Size() int {
+	return len(p.Population)
+}
+
+// newPopulation returns a pointer to a new, uninitialised population.
+func newPopulation(benchProblem string, np, dimen int) *Population {
+	p := &Population{
+		Problem:     benchProblem,
+		ProblemFunc: cec2020.Functions[benchProblem],
+		Dimen:       dimen,
+		Population:  make([]PopulationIndividual, np),
+	}
+
+	return p
+}

algo/ga/somat3a.go

@@ -0,0 +1,279 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package ga
+
+import (
+	"sort"
+	"time"
+
+	"git.dotya.ml/wanderer/math-optim/bench/cec2020"
+	"git.dotya.ml/wanderer/math-optim/stats"
+	"golang.org/x/exp/rand"
+	"gonum.org/v1/gonum/stat/distuv"
+)
+
+// SOMAT3A holds the settings for an instance of SOMA T3A algorithm.
+// nolint: unused
+type SOMAT3A struct {
+	// Generations is the number of generations to evolve for. Disable limit
+	// with -1.
+	Generations int
+	// BenchMinIters is the number of iterations that the bench function will
+	// be re-run.
+	BenchMinIters int
+	// Dimensions in which to look for a solution.
+	Dimensions []int
+	// NP is the initial population size.
+	NP int
+	// K is the number of individuals to choose the leader from.
+	K int
+	// M denotes how many individuals are picked from the population to form a
+	// `team` during the organisation phase, can be thought of as "team size".
+	M int
+	// N is the number of best individuals in each team selected for actual
+	// migration.
+	N int
+	// Njumps is the fixed number of jumps that each chosen migrating
+	// individual performs on their way to the leader.
+	Njumps int
+	// BenchName is the human-friendly name of the benchmarking function.
+	BenchName string
+	// ch is a channel for writing back computed results.
+	ch chan []stats.Stats
+	// chAlgoMeans is a channel for writing back algo means.
+	chAlgoMeans chan *stats.AlgoBenchMean
+
+	// rng is a random number generator.
+	rng distuv.Uniform
+
+	// initialised denotes the initialisation state of the struct.
+	initialised bool
+}
+
+var somat3aLogger = newLogger(" ***  SOMA T3A")
+
+// Init performs checks on the input params and initialises an instance of
+// SOMAT3A.
+func (s *SOMAT3A) Init(
+	generations, benchMinIters, np, k, m, n, njumps int,
+	dimensions []int,
+	bench string,
+	ch chan []stats.Stats,
+	chAlgoMeans chan *stats.AlgoBenchMean,
+) error {
+	switch {
+	case generations == 0:
+		return errGenerationsIsZero
+
+	case generations == -1:
+		somat3aLogger.Println("Generations is '-1', disabling generation limits")
+
+	case benchMinIters < 1:
+		return errBenchMinItersIsLTOne
+
+	case np == 0:
+		return errNPIsZero
+
+	case k == 0:
+		return errKIsZero
+
+	case m == 0:
+		return errMIsZero
+
+	case n == 0:
+		return errNIsZero
+
+	case njumps == 0:
+		return errNjumpsIsZero
+
+	case len(dimensions) == 0:
+		return errDimensionsUnset
+
+	case bench == "":
+		return errBenchUnset
+	}
+
+	s.Generations = generations
+	s.BenchMinIters = benchMinIters
+	s.NP = np
+	s.K = k
+	s.M = m
+	s.N = n
+	s.Njumps = njumps
+
+	s.Dimensions = dimensions
+	s.BenchName = bench
+	s.ch = ch
+	s.chAlgoMeans = chAlgoMeans
+
+	s.initialised = true
+
+	gaLogger.Printf("%s init done for bench %s", s.getAlgoName(), s.BenchName)
+
+	return nil
+}
+
+// Run runs the SOMA T3A algo for each of `s.Dimensions` with benchmarking
+// function `s.BenchName`, repeats the run `s.BenchMinIters` times and returns
+// the stats via chans.
+func (s *SOMAT3A) Run() {
+	if !s.initialised {
+		somat3aLogger.Fatalln(s.getAlgoName(), "needs to be initialised before calling Run(), exiting..")
+	}
+
+	var somat3aStats []stats.Stats
+	// testing purposes.
+	// s.BenchMinIters = 5
+
+	somat3aMeans := &stats.AlgoBenchMean{
+		Algo:       s.getAlgoName(),
+		BenchMeans: make([]stats.BenchMean, 0, len(s.Dimensions)),
+	}
+
+	for _, dim := range s.Dimensions {
+		maxFES := cec2020.GetMaxFES(dim)
+		// TODO(me): check the slides for the formula to calculate this.
+		// 4 fes per member per njump.
+		// fesPerIter := int(float64(maxFES / (4 * s.NP * s.Njumps)))
+		fesPerIter := int(float64(maxFES / (4 * s.NP)))
+		somat3aStatDimX := &stats.Stats{
+			Algo:        s.getAlgoName(),
+			Dimens:      dim,
+			Iterations:  s.BenchMinIters,
+			Generations: maxFES,
+			NP:          s.NP,
+		}
+		funcStats := &stats.FuncStats{BenchName: s.BenchName}
+		dimXMean := &stats.BenchMean{
+			Bench:       s.BenchName,
+			Dimens:      dim,
+			Iterations:  s.BenchMinIters,
+			Generations: maxFES,
+			Neighbours:  -1,
+		}
+		uniDist := distuv.Uniform{
+			Min: cec2020.SearchRange.Min(),
+			Max: cec2020.SearchRange.Max(),
+		}
+
+		if maxFES == -1 {
+			somat3aLogger.Fatalf("could not get maxFES for current dim (%d), bailing", dim)
+		}
+
+		somat3aLogger.Printf("running bench \"%s\" for %dD, maxFES: %d\n",
+			s.BenchName, dim, maxFES,
+		)
+
+		funcStats.BenchResults = make([]stats.BenchRound, s.BenchMinIters)
+
+		// rand.Seed(uint64(time.Now().UnixNano()))
+		// create and seed a source of pseudo-randomness
+		src := rand.NewSource(uint64(rand.Int63()))
+
+		// track execution duration.
+		start := time.Now()
+
+		for iter := 0; iter < s.BenchMinIters; iter++ {
+			somat3aLogger.Printf("run: %d, bench: %s, %dD, started at %s",
+				iter, s.BenchName, dim, start,
+			)
+
+			funcStats.BenchResults[iter].Iteration = iter
+
+			uniDist.Src = src
+			// uniDist.Src = rand.NewSource(uint64(rand.Int63()))
+
+			// create a population with known params.
+			pop := newSOMAT3APopulation(s.BenchName, s.NP, s.K, s.M, s.N, s.Njumps, dim)
+
+			// set population seed.
+			pop.Seed = uint64(time.Now().UnixNano())
+			// initialise the population.
+			pop.Init()
+
+			bestResult := s.evaluate(pop)
+
+			// the core.
+			for i := 0; i < fesPerIter; i++ {
+				funcStats.BenchResults[iter].Results = append(
+					funcStats.BenchResults[iter].Results,
+					bestResult,
+				)
+
+				// call evolve where the inner workngs of SOMA T3A run.
+				s.evolve(pop)
+
+				// take measurements of current population fitness.
+				r := s.evaluate(pop)
+
+				// save if better.
+				if r < bestResult {
+					bestResult = r
+				}
+
+				// this block makes sure we properly count func evaluations for
+				// the purpose of correctly comparable plot comparison. i.e.
+				// append the winning (current best) value 4*NP-1 (the first
+				// best is already saved at this point) times to represent the
+				// fact that while evaluating (and comparing) other population
+				// individuals to the current best value is taking place in the
+				// background, the current best value itself is kept around and
+				// symbolically saved as the best of the Generation.
+				for x := 0; x < 4*s.NP-1; x++ {
+					funcStats.BenchResults[iter].Results = append(
+						funcStats.BenchResults[iter].Results,
+						bestResult,
+					)
+				}
+			}
+		}
+
+		elapsed := time.Since(start)
+
+		somat3aLogger.Printf("completed: bench: %s, %dD, computing took %s\n",
+			s.BenchName, dim, elapsed,
+		)
+
+		// get mean vals.
+		dimXMean.MeanVals = stats.GetMeanVals(funcStats.BenchResults, maxFES)
+		funcStats.MeanVals = dimXMean.MeanVals
+
+		somat3aStatDimX.BenchFuncStats = append(
+			somat3aStatDimX.BenchFuncStats, *funcStats,
+		)
+
+		somat3aStats = append(somat3aStats, *somat3aStatDimX)
+
+		somat3aMeans.BenchMeans = append(somat3aMeans.BenchMeans, *dimXMean)
+	}
+
+	sort.Sort(somat3aMeans)
+
+	s.chAlgoMeans <- somat3aMeans
+	s.ch <- somat3aStats
+}
+
+func (s *SOMAT3A) evaluate(pop *SOMAT3APopulation) float64 {
+	bestIndividual := pop.Population[pop.GetBestIdx()]
+
+	return pop.ProblemFunc(bestIndividual.CurX)
+}
+
+func (s *SOMAT3A) evolve(pop *SOMAT3APopulation) {
+	// organise.
+	leader, migrants := pop.organise()
+	// migrate.
+	pop.migrate(leader, migrants)
+	// actualise (position, best values..).
+	pop.actualise(migrants)
+}
+
+func (s *SOMAT3A) getAlgoName() string {
+	return "SOMA T3A"
+}
+
+// NewSOMAT3A returns a pointer to a new, uninitialised SOMAT3A instance.
+func NewSOMAT3A() *SOMAT3A {
+	return &SOMAT3A{}
+}

algo/ga/somat3aActualisation.go

@@ -0,0 +1,31 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package ga
+
+func (p *SOMAT3APopulation) actualise(migrants []int) {
+	f := p.ProblemFunc
+
+	for _, i := range migrants {
+		bestJump := p.Population[i].Jumps[0]
+
+		for j := 1; j < p.Njumps; j++ {
+			jump := p.Population[i].Jumps[j]
+
+			// incrementing fes for the following is done in
+			// somat3aMigration.go.
+			if f(jump) < f(bestJump) {
+				bestJump = jump
+			}
+		}
+
+		if f(bestJump) < f(p.Population[i].CurX) {
+			p.Population[i].CurX = bestJump
+		}
+
+		p.fes += 2
+
+		// clean jump history
+		p.Population[i].Jumps = nil
+	}
+}

algo/ga/somat3aMigration.go

@@ -0,0 +1,80 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package ga
+
+import (
+	"git.dotya.ml/wanderer/math-optim/bench/cec2020"
+)
+
+// migration is used in SOMA-type algos.
+
+func (p *SOMAT3APopulation) migrate(leader int, migrants []int) {
+	for _, i := range migrants {
+		jumps := make([][]float64, p.Njumps)
+
+		// jump zero.
+		// jumps[0] = p.jump(i, leader, getStep(0, maxFES))
+
+		// bestJump of this migrant is now the jump zero.
+		// bestJump := jumps[0]
+
+		for j := 0; j < p.Njumps; j++ {
+			step := p.getStep()
+			jump := p.jump(i, leader, step)
+
+			p.fes += 2
+
+			jumps[j] = jump
+		}
+
+		p.Population[i].Jumps = jumps
+
+		p.Population[i].PRTVector = p.getPrtVector()
+	}
+
+	p.PRT = p.getPrt()
+}
+
+func (p *SOMAT3APopulation) jump(migrant, leader int, step float64) []float64 {
+	m := p.Population[migrant]
+	l := p.Population[leader]
+	prtv := m.PRTVector
+	t := step
+	jumpPosition := make([]float64, len(m.CurX))
+
+	for i := range m.CurX {
+		jumpPosition[i] = m.CurX[i] + ((l.CurX[i] - m.CurX[i]) * t * prtv[i])
+	}
+
+	return jumpPosition
+}
+
+// getPrtVector creates and returns the perturbation vector.
+func (p *SOMAT3APopulation) getPrtVector() []float64 {
+	prtv := make([]float64, p.Dimen)
+
+	for i := range prtv {
+		r := p.zeroToOneRNG.Rand()
+
+		switch {
+		case r < p.PRT:
+			prtv[i] = 1
+
+		case r > p.PRT:
+			prtv[i] = 0
+		}
+	}
+
+	return prtv
+}
+
+// getPrt calculates the adaptive PRT.
+func (p *SOMAT3APopulation) getPrt() float64 {
+	return 0.05 + 0.9*float64(p.fes/cec2020.GetMaxFES(p.Dimen))
+}
+
+// getStep calculates the step size.
+func (p *SOMAT3APopulation) getStep() float64 {
+	return 0.15 + 0.08*float64(p.fes/cec2020.GetMaxFES(p.Dimen))
+}

algo/ga/somat3aOrganisation.go

@@ -0,0 +1,76 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package ga
+
+import "golang.org/x/exp/rand"
+
+func (p *SOMAT3APopulation) organise() (int, []int) {
+	leader := p.selectLeader()
+	migrants := p.selectMigrants(leader)
+
+	return leader, migrants
+}
+
+func (p *SOMAT3APopulation) selectMigrants(leader int) []int {
+	m := make([]int, p.M)
+	n := make([]int, p.N)
+	popCount := len(p.Population)
+
+	m[0] = rand.Intn(popCount)
+
+	for i := 1; i < p.M; i++ {
+	mgen:
+		candidateM := rand.Intn(popCount)
+
+		for _, v := range m {
+			if candidateM == v {
+				goto mgen
+			}
+		}
+
+		m[i] = candidateM
+	}
+
+	n[0] = rand.Intn(p.M)
+
+	for i := 1; i < p.N; i++ {
+	ngen:
+		candidateN := rand.Intn(p.M)
+
+		for _, v := range n {
+			if candidateN == v || candidateN == leader {
+				goto ngen
+			}
+		}
+
+		n[i] = candidateN
+	}
+
+	return n
+}
+
+func (p *SOMAT3APopulation) selectLeader() int {
+	k := make([]int, p.K)
+	popCount := len(p.Population)
+
+	k[0] = rand.Intn(popCount)
+
+	for i := 1; i < p.K; i++ {
+	kgen:
+		candidate := rand.Intn(popCount)
+
+		for _, v := range k {
+			if candidate == v {
+				goto kgen
+			}
+		}
+
+		k[i] = candidate
+	}
+
+	// choose one of K to become the leader.
+	leader := k[rand.Intn(p.K)]
+
+	return leader
+}

algo/ga/somat3aPopulation.go

@@ -0,0 +1,174 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package ga
+
+import (
+	"git.dotya.ml/wanderer/math-optim/bench/cec2020"
+	"golang.org/x/exp/rand"
+
+	"gonum.org/v1/gonum/stat/distuv"
+)
+
+// PopulationIndividual represents a single population individual.
+type SOMAT3APopulationIndividual struct {
+	CurX      DecisionVector
+	PRTVector []float64
+	Jumps     [][]float64
+}
+
+// ChampionIndividual is a representation of the best individual currently
+// available in the population.
+type SOMAT3AChampionIndividual struct {
+	X DecisionVector
+}
+
+// Population groups population individuals (agents) with metadata about the population.
+type SOMAT3APopulation struct {
+	// Population is a slice of population individuals.
+	Population []SOMAT3APopulationIndividual
+	// Champion represents the best individual of the population.
+	Champion SOMAT3AChampionIndividual
+	// Problem is the current benchmarking function this population is attempting to optimise.
+	Problem string
+	// ProblemFunction is the actual function to optimise.
+	ProblemFunc func([]float64) float64
+	// Dimen is the dimensionality of the problem being optimised.
+	Dimen int
+	// fes is the current number of function evaluations.
+	fes int
+	// Seed is the value used to (re)init population.
+	Seed         uint64
+	zeroToOneRNG distuv.Uniform
+
+	// PRT is the perturbation parameter.
+	PRT float64
+	// K is the number of individuals to choose the leader from.
+	K int
+	// M denotes how many individuals are picked from the population to form a
+	// `team` during the organisation phase, can be thought of as "team size".
+	M int
+	// N is the number of best individuals in each team selected for actual
+	// migration.
+	N int
+	// Njumps is the fixed number of jumps that each chosen migrating
+	// individual performs on their way to the leader.
+	Njumps int
+}
+
+// GetIndividual returns a reference to individual at position n.
+func (p *SOMAT3APopulation) GetIndividual(n uint) *PopulationIndividual {
+	return &PopulationIndividual{}
+}
+
+// GetBestIdx returns the index of the best population individual.
+func (p *SOMAT3APopulation) GetBestIdx() int {
+	f := p.ProblemFunc
+
+	bestIndividual := 0
+	// the first one is the best one.
+	bestVal := f(p.Population[0].CurX)
+
+	for i, v := range p.Population {
+		current := f(v.CurX)
+
+		if current < bestVal {
+			bestIndividual = i
+		}
+	}
+
+	return bestIndividual
+}
+
+// GetWorstIdx returns the index of the worst population individual.
+func (p *SOMAT3APopulation) GetWorstIdx() int {
+	f := p.ProblemFunc
+
+	worstIndividual := 0
+	// the first one is the worst one.
+	worstVal := f(p.Population[0].CurX)
+
+	for i, v := range p.Population {
+		current := f(v.CurX)
+
+		if current > worstVal {
+			worstIndividual = i
+		}
+	}
+
+	return worstIndividual
+}
+
+// Init initialises all individuals to random values.
+func (p *SOMAT3APopulation) Init() {
+	low := cec2020.SearchRange.Min()
+	high := cec2020.SearchRange.Max()
+	// zeroToOneRNG := distuv.Uniform{
+	p.zeroToOneRNG = distuv.Uniform{
+		Min: 0,
+		Max: 1,
+		Src: rand.NewSource(p.Seed),
+	}
+
+	// gaLogger.Printf("population initialisation - popCount: %d, seed: %d\n",
+	// len(p.Population), p.Seed,
+	// )
+
+	p.PRT = p.getPrt()
+
+	// for i,v  := range p.Population {
+	for i := range p.Population {
+		v := SOMAT3APopulationIndividual{}
+
+		v.CurX = make([]float64, p.Dimen)
+
+		for j := 0; j < p.Dimen; j++ {
+			// with current settings, this will always be `low + 0` since
+			// `high - low = 0`.
+			v.CurX[j] = low + p.zeroToOneRNG.Rand()*(high-low)
+		}
+
+		v.PRTVector = p.getPrtVector()
+
+		v.Jumps = make([][]float64, p.Njumps)
+
+		p.Population[i] = v
+	}
+
+	p.Champion = SOMAT3AChampionIndividual{X: p.Population[p.GetBestIdx()].CurX}
+}
+
+// Reinit reinitialises all individuals.
+func (p *SOMAT3APopulation) Reinit() {
+	p.Init()
+}
+
+// Clear sets all vectors to 0.
+func (p *SOMAT3APopulation) Clear() {
+	if p.Population != nil {
+		for _, v := range p.Population {
+			v.CurX = make([]float64, p.Dimen)
+		}
+	}
+}
+
+// Size returns the number of population individuals.
+func (p *SOMAT3APopulation) Size() int {
+	return len(p.Population)
+}
+
+// newPopulation returns a pointer to a new, uninitialised population.
+func newSOMAT3APopulation(benchProblem string, np, k, m, n, nj, dimen int) *SOMAT3APopulation {
+	p := &SOMAT3APopulation{
+		Problem:     benchProblem,
+		ProblemFunc: cec2020.Functions[benchProblem],
+		K:           k,
+		M:           m,
+		N:           n,
+		Njumps:      nj,
+		Dimen:       dimen,
+		Population:  make([]SOMAT3APopulationIndividual, np),
+	}
+
+	return p
+}

algo/plot.go

@@ -6,6 +6,7 @@
 import (
 	"fmt"
 	"log"
+	"strconv"
 	"strings"
 	"sync"
 	"time"
@@ -23,8 +24,9 @@
 const (
 	preferredFont      = "Mono"
 	titlePreferredFont = "Sans"
-	yAxisLabel         = "CF value"
+	yAxisLabel         = "f(x)"
 	xAxisLabel         = "Generations"
+	xAxisLabelAlt      = "FES"
 )
 
 // PlotMeanValsMulti creates plots for every member of 'stats.AlgoMeanVals' it
@@ -55,7 +57,8 @@ func PlotMeanValsMulti(
 		iterations, bench, dimens,
 	)
 
-	p.X.Label.Text = "Objective func. evaluations"
+	// p.X.Label.Text = "Objective func. evaluations"
+	p.X.Label.Text = xAxisLabelAlt
 	p.X.Label.TextStyle.Font.Variant = preferredFont
 	p.X.Label.TextStyle.Font.Weight = 1 // Medium
 	p.X.Tick.Label.Font.Variant = preferredFont
@@ -149,7 +152,7 @@ func PlotMeanValsMulti(
 	mCoMPL.Unlock()
 }
 
-func plotMeanVals(meanVals []float64, title string, fes int) *plot.Plot {
+func plotMeanVals(meanVals []float64, title, algo string, fes int) *plot.Plot {
 	plotter.DefaultFont.Typeface = preferredFont
 	plotter.DefaultLineStyle.Width = vg.Points(2.0)
 
@@ -162,7 +165,12 @@ func plotMeanVals(meanVals []float64, title string, fes int) *plot.Plot {
 
 	p.Title.Text = "Mean - " + title
 
-	p.X.Label.Text = xAxisLabel
+	if strings.Contains(algo, "DE") || strings.Contains(algo, "SOMA") {
+		p.X.Label.Text = xAxisLabelAlt
+	} else {
+		p.X.Label.Text = xAxisLabel
+	}
+
 	// p.X.Label.Padding = 8 * vg.Millimeter
 	p.X.Label.TextStyle.Font.Variant = preferredFont
 	p.X.Label.TextStyle.Font.Weight = 1 // Medium
@@ -208,8 +216,8 @@ func plotMeanVals(meanVals []float64, title string, fes int) *plot.Plot {
 	return p
 }
 
-// violating gocognit 30, TODO(me): still split this up.
-// nolint: gocognit
+// violating gocognit 30, TODO(me): still split this up. fPrefix is always "plot" according to unparam.
+// nolint: gocognit,unparam
 func plotAllDims(algoStats []stats.Stats, fPrefix, fExt string, ch chan report.PicList, chMean chan report.PicList) {
 	start := time.Now()
 
@@ -246,6 +254,14 @@ func plotAllDims(algoStats []stats.Stats, fPrefix, fExt string, ch chan report.P
 			s.Generations,
 			s.Iterations,
 		)
+
+		// For differential evolution, add params to title.
+		if strings.Contains(s.Algo, "DE") {
+			p.Title.Text += fmt.Sprintf(",\nNP: %d, F: ", s.NP) +
+				strconv.FormatFloat(s.F, 'f', -1, 64) + ", CR: " +
+				strconv.FormatFloat(s.CR, 'f', -1, 64)
+		}
+
 		// this is latex-rendered.
 		pic.Caption = strings.ReplaceAll(p.Title.Text, " ", "~")
 
@@ -254,7 +270,12 @@ func plotAllDims(algoStats []stats.Stats, fPrefix, fExt string, ch chan report.P
 		pL.Bench = s.BenchFuncStats[0].BenchName
 		pLMean.Bench = s.BenchFuncStats[0].BenchName
 
-		p.X.Label.Text = xAxisLabel
+		if strings.Contains(s.Algo, "DE") {
+			p.X.Label.Text = xAxisLabelAlt
+		} else {
+			p.X.Label.Text = xAxisLabel
+		}
+
 		p.X.Label.TextStyle.Font.Variant = preferredFont
 		p.X.Label.TextStyle.Font.Weight = 1 // Medium
 		p.X.Tick.Label.Font.Variant = preferredFont
@@ -310,6 +331,8 @@ func plotAllDims(algoStats []stats.Stats, fPrefix, fExt string, ch chan report.P
 				lines...,
 			)
 			if err != nil {
+				log.Printf("issue constructing a plot, bench: %s", dim.BenchName)
+
 				// panic (don't panic, I know) instead of a hard exit.
 				log.Panic(err)
 			}
@@ -343,18 +366,22 @@ func plotAllDims(algoStats []stats.Stats, fPrefix, fExt string, ch chan report.P
 			picMean.FilePath = filenameMean
 
 			// get the *mean* plot.
-			pMean := plotMeanVals(dim.MeanVals, meanTitle, s.Generations)
+			pMean := plotMeanVals(dim.MeanVals, meanTitle, s.Algo, s.Generations)
 
 			elapsed := time.Since(start)
 			info := fmt.Sprintf("saving img to file: %s(-Mean)%s [generated in %s]",
 				filename, fExt, elapsed,
 			)
 
-			// TODO(me): rework this.
-			if s.Algo == "Random Search" {
+			switch {
+			case s.Algo == "Random Search":
 				printRandomSearch(info)
-			} else {
+
+			case strings.Contains(s.Algo, "Stochastic Hill Climbing"):
 				printSHC(info)
+
+			default:
+				log.Println(info)
 			}
 
 			// Save the plot to a file using the above-constructed 'filename'.

bench/bench.go

@@ -3,6 +3,8 @@
 
 package bench
 
+import "log"
+
 type funcParams struct {
 	min float64
 	max float64
@@ -23,6 +25,9 @@ type funcParams struct {
 	// Dimensions to compute for (spatial complexity..?).
 	Dimensions = []int{5, 10, 20}
 
+	// DimensionsGA are used with Genetic Algorithms (such as DE, see algo/de).
+	DimensionsGA = []int{10, 30}
+
 	// SchwefelParams is a struct holding the min, max allowed value of inputs
 	// passed to the Schwefel function.
 	SchwefelParams = funcParams{min: -500.0, max: 500.0}
@@ -32,6 +37,9 @@ type funcParams struct {
 	// DeJong2Params is a struct holding the min, max allowed value of inputs
 	// passed to the De Jong 2nd function.
 	DeJong2Params = funcParams{min: -5.0, max: 5.0}
+	// RastriginParams is a struct holding the min, max allowed value of inputs
+	// passed to the Rastrigin function.
+	RastriginParams = funcParams{min: -5.12, max: 5.12}
 )
 
 // Min returns the non-exported "min" field of a funcParams struct.
@@ -43,3 +51,19 @@ func (f *funcParams) Min() float64 {
 func (f *funcParams) Max() float64 {
 	return f.max
 }
+
+// NewfuncParams returns a pointer to a fresh instance of funcParams.
+// nolint: revive
+func NewfuncParams(min, max float64) *funcParams {
+	return &funcParams{min: min, max: max}
+}
+
+// GetGAMaxFES calculates the value of MaxFES for Genetic Algorithms. This is
+// an arbitrary specification where MaxFES is 5000xD.
+func GetGAMaxFES(dim int) int {
+	if dim <= 0 {
+		log.Fatalln("dim has to be greater than 0, got:", dim)
+	}
+
+	return dim * 5000
+}

bench/bench_test.go

@@ -62,3 +62,13 @@ func TestDeJong2Params(t *testing.T) {
 		t.Errorf("wrong DeJong2Params, want: %+v, got: %+v", want, got)
 	}
 }
+
+func TestGetGAMaxFES(t *testing.T) {
+	dim := 3
+	want := 15000
+	got := GetGAMaxFES(dim)
+
+	if want != got {
+		t.Errorf("wrong GAMaxFES result, want: %d, got: %d", want, got)
+	}
+}

bench/cec2020/basicFunctions.go

@@ -0,0 +1,212 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package cec2020
+
+import "math"
+
+// Rastrigin calculates the value of the Rastrigin function for x.
+func Rastrigin(x []float64) float64 {
+	var sum float64
+
+	for i := range x {
+		sum += math.Pow(x[i], 2) - (10 * math.Cos(2*math.Pi*x[i])) + 10
+	}
+
+	return sum
+}
+
+// HighConditionedElliptic is the "High Conditioned Elliptic Function" of
+// CEC2020.
+func HighConditionedElliptic(x []float64) float64 {
+	var sum float64
+
+	// float64 version of the length of x.
+	fnx := float64(len(x))
+
+	for i := range x {
+		sum += math.Pow(math.Pow(10, 6), float64(i-1)/(fnx-1)) * math.Pow(x[i], 2)
+	}
+
+	return sum
+}
+
+// HGBat is the "HGBat Function" of CEC2020.
+func HGBat(x []float64) float64 {
+	var sum1 float64
+
+	var sum2 float64
+
+	for i := range x {
+		sum1 += math.Pow(x[i], 2)
+
+		sum2 += x[i]
+	}
+
+	// float64 version of the length of x.
+	fnx := float64(len(x))
+
+	return math.Pow(math.Abs(math.Pow(sum1, 2)-math.Pow(sum2, 2)), 0.5) + (((0.5 * sum1) + sum2) / (fnx + 0.5))
+}
+
+// Rosenbrock is the "Rosenbrock's Function" of CEC2020.
+// ref: https://infinity77.net/global_optimization/test_functions_nd_R.html#go_benchmark.Rosenbrock
+func Rosenbrock(x []float64) float64 {
+	var sum float64
+
+	for i := 0; i < len(x)-1; i++ {
+		sum += 100*(math.Pow(math.Pow(x[i], 2)-x[i+1], 2)) + math.Pow(x[i]-1, 2)
+	}
+
+	return sum
+}
+
+// Griewank is the "Griewank's Function" of CEC2020.
+// ref: https://www.sfu.ca/~ssurjano/griewank.html
+func Griewank(x []float64) float64 {
+	var sum float64
+
+	var prod float64
+
+	for i := range x {
+		sum += math.Pow(x[i], 2) / 4000
+		// use i+1 in sqrt so that we don't get Nan.
+		prod *= math.Cos(x[i] / math.Sqrt(float64(i+1)))
+	}
+
+	return sum - prod + 1
+}
+
+// Ackley is the "Ackley's Function" of CEC2020.
+func Ackley(x []float64) float64 {
+	var sum1 float64
+
+	var sum2 float64
+
+	for i := range x {
+		sum1 += math.Pow(x[i], 2)
+
+		sum2 += math.Cos(2 * math.Pi * x[i])
+	}
+
+	// float64 version of the length of x.
+	fnx := float64(len(x))
+
+	return -20*math.Exp(-0.2*math.Sqrt((1/fnx)*sum1)) - math.Exp((1/fnx)*sum2) + 20 + math.E
+}
+
+// Happycat is the "Happycat Function" of CEC2020.
+func Happycat(x []float64) float64 {
+	var sum1 float64
+
+	var sum2 float64
+
+	var sum3 float64
+
+	// float64 version of the length of x.
+	fnx := float64(len(x))
+
+	for i := range x {
+		xipow2 := math.Pow(x[i], 2)
+
+		sum1 += xipow2 - fnx
+
+		sum2 += xipow2
+
+		sum3 += x[i]
+	}
+
+	return math.Pow(math.Abs(sum1), 0.25) + (((0.5 * sum2) + sum3) / (fnx + 0.5))
+}
+
+// Discus is the "Discus Function" of CEC2020.
+func Discus(x []float64) float64 {
+	var sum float64
+
+	nx := len(x)
+
+	for i := 1; i < nx; i++ {
+		sum += math.Pow(x[i], 2)
+	}
+
+	return sum + (1000000 * math.Pow(x[0], 2))
+}
+
+// SchwefelModified is the "Modified Schwefel's Function" of CEC2020 with summation
+// of g(zi) where zi = xi + 4.209687462275036e+002 and
+// g(zi) = zi * sin(|zi|^(1/2)) ... if |zi| <= 500,
+// g(zi) = (500-mod(zi,500)) * sin(sqrt(|500-mod(zi,500)|)) - (zi-500^2)/10000D ... if zi > 500,
+// g(zi) = (mod(|zi|,500)-500) * sin(sqrt(|mod(|zi|,500)-500|)) - (zi-500^2)/10000D ... if zi < -500.
+func SchwefelModified(x []float64) float64 {
+	var sum float64
+
+	// float64 version of the length of x.
+	fnx := float64(len(x))
+
+	for i := range x {
+		zi := x[i] + 4.209687462275036e+002
+
+		switch {
+		case zi > 500:
+			// g(zi)
+			sum += (500-math.Mod(zi, 500))*math.Sin(math.Sqrt(math.Abs(500-math.Mod(zi, 500)))) - (math.Pow(zi-500.0, 2.0) - 10000*fnx)
+
+		case zi < -500:
+			// g(zi)
+
+		case math.Abs(zi) <= 500:
+			// g(zi)
+			sum += zi * math.Sin(math.Pow(math.Abs(zi), 0.5))
+		}
+	}
+
+	return 418.9829*fnx - sum
+}
+
+// Schaffer is the Schaffer's function.
+func Schaffer(x, y float64) float64 {
+	return 0.5 + (math.Pow(math.Sin(math.Sqrt(math.Pow(x, 2)+math.Pow(y, 2))-0.5), 2) / (1 + 0.001*(math.Pow(x, 2)+math.Pow(y, 2))))
+}
+
+// SchafferExpanded is the "Expanded Schaffer's Function" of CEC2020.
+func SchafferExpanded(x []float64) float64 {
+	var sum float64
+
+	nx := len(x)
+
+	for i := range x {
+		sum += Schaffer(x[i], x[i%nx])
+	}
+
+	return sum
+}
+
+// Weierstrass is the "Weierstrass Function" of CEC2020 with a=0.5, b=3 and
+// kmax=20.
+func Weierstrass(x []float64) float64 {
+	a := 0.5
+	b := 3.0
+	kmax := 20.0
+	// float64 version of the length of x.
+	fnx := float64(len(x))
+
+	var sum1 float64
+
+	var sum2 float64
+
+	for i := range x {
+		var ksum float64
+
+		for k := 0.0; k < kmax; k++ {
+			ksum += math.Pow(a, k) * math.Cos(2*math.Pi*math.Pow(b, k)*(x[i]+0.5))
+		}
+
+		sum1 += ksum
+	}
+
+	for k := 0.0; k < kmax; k++ {
+		sum2 += math.Pow(a, k) * math.Cos(2*math.Pi*math.Pow(b, k)*0.5)
+	}
+
+	return sum1 - (fnx * sum2)
+}

bench/cec2020/basicFunctions_test.go

@@ -0,0 +1,247 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package cec2020
+
+import "testing"
+
+// TestRastrigin checks that the Rastrigin func performs as expected.
+func TestRastrigin(t *testing.T) {
+	testInput := []float64{
+		-2.7137778065280593,
+		-1.9789783390895765,
+		-4.38267902085263,
+		-4.617877608383319,
+		-2.1529909200665474,
+		-3.801849324611571,
+		2.4968697503272486,
+		-0.27358100201730196,
+		2.0444319451255977,
+		3.8798952350630653,
+		-0.6150024279011337,
+		2.8643010641852413,
+		-0.6692444051928748,
+		4.4649633199687475,
+		3.2510298731558507,
+		-4.2095036422081495,
+		-0.130882052243404,
+		1.2001716295708604,
+		-0.29427650577602193,
+	}
+	want := 343.97127081044846
+	got := Rastrigin(testInput)
+
+	if want != got {
+		t.Errorf("incorrect Rastrigin output, want: %f, got: %f", want, got)
+	}
+}
+
+func TestHighConditionedElliptic(t *testing.T) {
+	testInput := []float64{
+		-2.7137778065280593,
+		-1.9789783390895765,
+		-4.38267902085263,
+		-4.617877608383319,
+		-2.1529909200665474,
+		-3.801849324611571,
+		2.4968697503272486,
+		-0.27358100201730196,
+		2.0444319451255977,
+		3.8798952350630653,
+		-0.6150024279011337,
+		2.8643010641852413,
+		-0.6692444051928748,
+		4.4649633199687475,
+		3.2510298731558507,
+		-4.2095036422081495,
+		-0.130882052243404,
+		1.2001716295708604,
+		-0.29427650577602193,
+	}
+	want := 1.630609804125232e+06
+	got := HighConditionedElliptic(testInput)
+
+	if want != got {
+		t.Errorf("incorrect output, want: %f, got: %v", want, got)
+	}
+}
+
+func TestGHBat(t *testing.T) {
+	testInput := []float64{
+		-2.7137778065280593,
+		-1.9789783390895765,
+		-4.38267902085263,
+		-4.617877608383319,
+		-2.1529909200665474,
+		-3.801849324611571,
+		2.4968697503272486,
+		-0.27358100201730196,
+		2.0444319451255977,
+		3.8798952350630653,
+		-0.6150024279011337,
+		2.8643010641852413,
+		-0.6692444051928748,
+		4.4649633199687475,
+		3.2510298731558507,
+		-4.2095036422081495,
+		-0.130882052243404,
+		1.2001716295708604,
+		-0.29427650577602193,
+	}
+	want := 158.83404437332385
+	got := HGBat(testInput)
+
+	if want != got {
+		t.Errorf("incorrect output, want: %f, got: %v", want, got)
+	}
+}
+
+func TestRosenbrock(t *testing.T) {
+	testInput := []float64{
+		-2.7137778065280593,
+		-1.9789783390895765,
+		-4.38267902085263,
+		-4.617877608383319,
+		-2.1529909200665474,
+		-3.801849324611571,
+		2.4968697503272486,
+		-0.27358100201730196,
+		2.0444319451255977,
+		3.8798952350630653,
+		-0.6150024279011337,
+		2.8643010641852413,
+		-0.6692444051928748,
+		4.4649633199687475,
+		3.2510298731558507,
+		-4.2095036422081495,
+		-0.130882052243404,
+		1.2001716295708604,
+		-0.29427650577602193,
+	}
+	want := 270378.6217883186
+	got := Rosenbrock(testInput)
+
+	if want != got {
+		t.Errorf("incorrect output, want: %f, got: %v", want, got)
+	}
+}
+
+func TestAckley(t *testing.T) {
+	testInput := []float64{
+		-2.7137778065280593,
+		-1.9789783390895765,
+		-4.38267902085263,
+		-4.617877608383319,
+		-2.1529909200665474,
+		-3.801849324611571,
+		2.4968697503272486,
+		-0.27358100201730196,
+		2.0444319451255977,
+		3.8798952350630653,
+		-0.6150024279011337,
+		2.8643010641852413,
+		-0.6692444051928748,
+		4.4649633199687475,
+		3.2510298731558507,
+		-4.2095036422081495,
+		-0.130882052243404,
+		1.2001716295708604,
+		-0.29427650577602193,
+	}
+	want := 10.420166703127961
+	got := Ackley(testInput)
+
+	if want != got {
+		t.Errorf("incorrect output, want: %f, got: %v", want, got)
+	}
+}
+
+func TestDiscus(t *testing.T) {
+	testInput := []float64{
+		-2.7137778065280593,
+		-1.9789783390895765,
+		-4.38267902085263,
+		-4.617877608383319,
+		-2.1529909200665474,
+		-3.801849324611571,
+		2.4968697503272486,
+		-0.27358100201730196,
+		2.0444319451255977,
+		3.8798952350630653,
+		-0.6150024279011337,
+		2.8643010641852413,
+		-0.6692444051928748,
+		4.4649633199687475,
+		3.2510298731558507,
+		-4.2095036422081495,
+		-0.130882052243404,
+		1.2001716295708604,
+		-0.29427650577602193,
+	}
+	want := 7.364737863641793e+06
+	got := Discus(testInput)
+
+	if want != got {
+		t.Errorf("incorrect output, want: %f, got: %v", want, got)
+	}
+}
+
+func TestSchafferExpanded(t *testing.T) {
+	testInput := []float64{
+		-2.7137778065280593,
+		-1.9789783390895765,
+		-4.38267902085263,
+		-4.617877608383319,
+		-2.1529909200665474,
+		-3.801849324611571,
+		2.4968697503272486,
+		-0.27358100201730196,
+		2.0444319451255977,
+		3.8798952350630653,
+		-0.6150024279011337,
+		2.8643010641852413,
+		-0.6692444051928748,
+		4.4649633199687475,
+		3.2510298731558507,
+		-4.2095036422081495,
+		-0.130882052243404,
+		1.2001716295708604,
+		-0.29427650577602193,
+	}
+	want := 15.903821248304322
+	got := SchafferExpanded(testInput)
+
+	if want != got {
+		t.Errorf("incorrect output, want: %f, got: %v", want, got)
+	}
+}
+
+func TestWeierstrass(t *testing.T) {
+	testInput := []float64{
+		-2.7137778065280593,
+		-1.9789783390895765,
+		-4.38267902085263,
+		-4.617877608383319,
+		-2.1529909200665474,
+		-3.801849324611571,
+		2.4968697503272486,
+		-0.27358100201730196,
+		2.0444319451255977,
+		3.8798952350630653,
+		-0.6150024279011337,
+		2.8643010641852413,
+		-0.6692444051928748,
+		4.4649633199687475,
+		3.2510298731558507,
+		-4.2095036422081495,
+		-0.130882052243404,
+		1.2001716295708604,
+		-0.29427650577602193,
+	}
+	want := 39.69284047936819
+	got := Weierstrass(testInput)
+
+	if want != got {
+		t.Errorf("incorrect output, want: %f, got: %v", want, got)
+	}
+}

bench/cec2020/benchFunctions.go

@@ -0,0 +1,263 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package cec2020
+
+import "math"
+
+// BentCigar is the "Bent Cigar Function" of CEC2020.
+func BentCigar(x []float64) float64 {
+	var sum float64
+
+	for i := 1; i < len(x); i++ {
+		sum += math.Pow(x[i], 2)
+	}
+
+	return (1000000 * sum) + math.Pow(x[0], 2)
+}
+
+// Schwefel is the "Shifted and Rotated Schwefel's Function" of CEC2020.
+func Schwefel(x []float64) float64 {
+	return SchwefelModified(x)
+}
+
+// LunacekBiRastrigin is the "Shifted and Rotated Lunacek bi-Rastrigin Function" of CEC2020.
+// ref: https://al-roomi.org/benchmarks/unconstrained/n-dimensions/229-lunacek-s-bi-rastrigin-function.
+func LunacekBiRastrigin(x []float64) float64 {
+	var sum0 float64
+
+	var sum1 float64
+
+	var sum2 float64
+
+	nx := len(x)
+	fnx := float64(nx)
+	s := 1 - (1 / ((2 * math.Sqrt(fnx+20)) - 8.2))
+	d := 1.0
+	mu0 := 2.5
+	mu1 := -math.Sqrt((math.Pow(mu0, 2) - d) / s)
+	xhat := getMean(x)
+
+	for i := range x {
+		sum0 += math.Pow(xhat-mu0, 2)
+
+		sum1 += math.Pow(xhat-mu1, 2)
+
+		zi := math.Pow(100, 0.5*((float64(i)-1)/fnx-1)) * xhat
+
+		sum2 += (1 - math.Cos(2*math.Pi*zi))
+	}
+
+	return math.Min(sum0, (d*fnx)+(s*sum1)) + 10*sum2
+}
+
+// RosenbrockGriewank is the "Expanded Rosenbrock's plus Griewank's Function"
+// of CEC2020.
+func RosenbrockGriewank(x []float64) float64 {
+	var sum float64
+
+	nx := len(x)
+
+	for i := range x {
+		f := []float64{x[i], x[(i+1)%nx]}
+
+		sum += Griewank([]float64{Rosenbrock(f)})
+	}
+
+	return sum
+}
+
+// Hybrid1 is the "Hybrid Function 1" of CEC2020.
+func Hybrid1(x []float64) float64 {
+	nx := len(x)
+	fnx := float64(nx)
+
+	funcs := []func([]float64) float64{
+		SchwefelModified,
+		Rastrigin,
+		HighConditionedElliptic,
+	}
+	// percentages used to control the amount of contribution of each func.
+	p := []float64{0.3, 0.3, 0.4}
+	gnx := getGnx(p, fnx)
+
+	var sum float64
+
+	for i := range funcs {
+		sum += funcs[i](x) * gnx[i]
+	}
+
+	return sum
+}
+
+// Hybrid2 is the "Hybrid Function 2" of CEC2020.
+func Hybrid2(x []float64) float64 {
+	nx := len(x)
+	fnx := float64(nx)
+
+	funcs := []func([]float64) float64{
+		SchafferExpanded,
+		HGBat,
+		Rosenbrock,
+		SchwefelModified,
+	}
+	// percentages used to control the amount of contribution of each func.
+	p := []float64{0.2, 0.2, 0.3, 0.3}
+	gnx := getGnx(p, fnx)
+
+	var sum float64
+
+	for i := range funcs {
+		sum += funcs[i](x) * gnx[i]
+	}
+
+	return sum
+}
+
+// Hybrid3 is the "Hybrid Function 3" of CEC2020.
+func Hybrid3(x []float64) float64 {
+	nx := len(x)
+	fnx := float64(nx)
+
+	funcs := []func([]float64) float64{
+		SchafferExpanded,
+		HGBat,
+		Rosenbrock,
+		SchwefelModified,
+		HighConditionedElliptic,
+	}
+	// percentages used to control the amount of contribution of each func.
+	p := []float64{0.1, 0.2, 0.2, 0.2, 0.3}
+	gnx := getGnx(p, fnx)
+
+	var sum float64
+
+	for i := range funcs {
+		sum += funcs[i](x) * gnx[i]
+	}
+
+	return sum
+}
+
+// Composition1 is the "Composition Function 1" of CEC2020.
+func Composition1(x []float64) float64 {
+	nx := len(x)
+	fnx := float64(nx)
+
+	// optimum positions.
+	o := newOpt(nx)
+	sigma := []float64{10, 20, 30}
+	lambda := []float64{1, 10, 1}
+	bias := []float64{0, 100, 200}
+	omega := make([]float64, nx)
+	funcs := []func([]float64) float64{
+		Rastrigin,
+		Griewank,
+		SchwefelModified,
+	}
+
+	var sum float64
+
+	weights := make([]float64, len(funcs))
+
+	for i := range funcs {
+		wi := getWeight(x, o, sigma[i], fnx)
+
+		weights = append(weights, wi)
+
+		var wsum float64
+
+		for j := range weights {
+			wsum += weights[j]
+		}
+
+		omega = append(omega, wi/wsum)
+
+		sum += (omega[i] * (lambda[i]*funcs[i](x) + bias[i])) + funcs[i](x)
+	}
+
+	return sum
+}
+
+// Composition2 is the "Composition Function 2" of CEC2020.
+func Composition2(x []float64) float64 {
+	nx := len(x)
+	fnx := float64(nx)
+
+	// optimum positions.
+	o := newOpt(nx)
+	sigma := []float64{10, 20, 30, 40}
+	lambda := []float64{10, 1e-6, 10, 1}
+	bias := []float64{0, 100, 200, 300}
+	omega := make([]float64, nx)
+	funcs := []func([]float64) float64{
+		Ackley,
+		HighConditionedElliptic,
+		Griewank,
+		Rastrigin,
+	}
+
+	var sum float64
+
+	weights := make([]float64, len(funcs))
+
+	for i := range funcs {
+		wi := getWeight(x, o, sigma[i], fnx)
+
+		weights = append(weights, wi)
+
+		var wsum float64
+
+		for j := range weights {
+			wsum += weights[j]
+		}
+
+		omega = append(omega, wi/wsum)
+
+		sum += (omega[i] * (lambda[i]*funcs[i](x) + bias[i])) + funcs[i](x)
+	}
+
+	return sum
+}
+
+// Composition3 is the "Composition Function 3" of CEC2020.
+func Composition3(x []float64) float64 {
+	nx := len(x)
+	fnx := float64(nx)
+
+	// optimum positions.
+	o := newOpt(nx)
+	sigma := []float64{10, 20, 30, 40, 50}
+	lambda := []float64{10, 1, 10, 1e-6, 1}
+	bias := []float64{0, 100, 200, 300, 400}
+	omega := make([]float64, nx)
+	funcs := []func([]float64) float64{
+		Rastrigin,
+		Happycat,
+		Ackley,
+		Discus,
+		Rosenbrock,
+	}
+
+	var sum float64
+
+	weights := make([]float64, len(funcs))
+
+	for i := range funcs {
+		wi := getWeight(x, o, sigma[i], fnx)
+
+		weights = append(weights, wi)
+
+		var wsum float64
+
+		for j := range weights {
+			wsum += weights[j]
+		}
+
+		omega = append(omega, wi/wsum)
+
+		sum += (omega[i] * (lambda[i]*funcs[i](x) + bias[i])) + funcs[i](x)
+	}
+
+	return sum
+}

bench/cec2020/benchFunctions_test.go

@@ -0,0 +1,83 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package cec2020
+
+import "testing"
+
+func TestComposition1(t *testing.T) {
+	in := []float64{-9.2, 10, .7, 3, -80.2843}
+	want := 7248.007126969505
+
+	got := Composition1(in)
+
+	if got != want {
+		t.Errorf("unexpected result from Composition1, want: %f, got: %v", want, got)
+	}
+}
+
+func TestComposition2(t *testing.T) {
+	in := []float64{-9.2, 10, .7, 3, -80.2843}
+	want := 2.0384259701506793e+08
+
+	got := Composition2(in)
+
+	if got != want {
+		t.Errorf("unexpected result from Composition2, want: %f, got: %v", want, got)
+	}
+}
+
+func TestComposition3(t *testing.T) {
+	in := []float64{-9.2, 10, .7, 3, -80.2843}
+	want := 8.699499674241544e+07
+
+	got := Composition3(in)
+
+	if got != want {
+		t.Errorf("unexpected result from Composition3, want: %f, got: %v", want, got)
+	}
+}
+
+func TestGriewank(t *testing.T) {
+	in := []float64{-9.2, 10, .7, 3, -80.2843}
+	want := 2.6599247066225002
+
+	got := Griewank(in)
+
+	if got != want {
+		t.Errorf("unexpected result from Griewank, want: %f, got: %v", want, got)
+	}
+}
+
+func TestHybrid1(t *testing.T) {
+	in := []float64{-9.2, 10, .7, 3, -80.2843}
+	want := 2.0385039193698734e+08
+
+	got := Hybrid1(in)
+
+	if got != want {
+		t.Errorf("unexpected result from Hybrid1, want: %f, got: %v", want, got)
+	}
+}
+
+func TestHybrid2(t *testing.T) {
+	in := []float64{-9.2, 10, .7, 3, -80.2843}
+	want := 4.6901057350488175e+06
+
+	got := Hybrid2(in)
+
+	if got != want {
+		t.Errorf("unexpected result from Hybrid2, want: %f, got: %v", want, got)
+	}
+}
+
+func TestHybrid3(t *testing.T) {
+	in := []float64{-9.2, 10, .7, 3, -80.2843}
+	want := 2.0618485725498265e+08
+
+	got := Hybrid3(in)
+
+	if got != want {
+		t.Errorf("unexpected result from Hybrid3, want: %f, got: %v", want, got)
+	}
+}

bench/cec2020/constants.go

@@ -0,0 +1,16 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package cec2020
+
+/*
+#define INF 1.0e99
+#define EPS 1.0e-14
+#define E  2.7182818284590452353602874713526625
+#define PI 3.1415926535897932384626433832795029
+*/
+
+const (
+	searchRangeMin = -100
+	searchRangeMax = 100
+)

bench/cec2020/doc.go

@@ -0,0 +1,7 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+// Package cec2020 implements functions of the CEC2020 Testbed.
+// Function definitions and overview: https://github.com/P-N-Suganthan/2020-Bound-Constrained-Opt-Benchmark/blob/d8b4c52f161562cd462e9b3352885e8df6fd2e41/Definitions%20of%20%20CEC2020%20benchmark%20suite%20Bound%20Constrained.pdf
+// Example implementation: https://github.com/danney9512/CEC2020-Bound-Constrained-Opt_mpmL-SHADE/blob/master/mpmL-SHADE_source code/src/problem_test_functions.h
+package cec2020

bench/cec2020/helperFunctions.go

@@ -0,0 +1,203 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package cec2020
+
+import (
+	"math"
+	"time"
+
+	"golang.org/x/exp/rand"
+
+	"gonum.org/v1/gonum/stat"
+	"gonum.org/v1/gonum/stat/distuv"
+)
+
+// void shiftfunc (double*,double*,int,double*);
+// void rotatefunc (double*,double*,int, double*);
+// void sr_func (double *, double *, int, double*, double*, double, int, int); // shift and rotate
+// void asyfunc (double *, double *x, int, double);
+// void oszfunc (double *, double *, int);
+// void cf_cal(double *, double *, int, double *,double *,double *,double *,int);
+
+// Shift shifts values of x based on values of os.
+func Shift(x []float64, os []float64) {
+	if len(x) != len(os) {
+		cec2020Logger.Fatalln("slices are of different sizes, bailing...")
+	}
+
+	for i := range x {
+		x[i] -= os[i]
+	}
+}
+
+// Rotate rotates values of x based on values of mr.
+func Rotate(x []float64, mr []float64) {
+	nx := len(x)
+
+	if int(math.Pow(float64(nx), 2)) > len(mr) {
+		cec2020Logger.Fatalf("either x is too big or mr is of insufficient size (%d:%d), bailing...\n", nx, len(mr))
+	}
+
+	xrot := make([]float64, nx)
+
+	for i := 0; i < nx; i++ {
+		for j := 0; j < nx; j++ {
+			x[j] += xrot[i] + (x[j] * mr[(i*nx)+j])
+		}
+	}
+}
+
+// ShiftRotate shifts and/or rotates the input x based on the request params
+// shift and rotate (bool), using the values of os and/or mr.
+func ShiftRotate(x []float64, os []float64, mr []float64, shiftRate float64, shift, rotate bool) {
+	switch {
+	case shift && rotate:
+		Shift(x, os)
+
+		// shrink to the original search range.
+		for i := range x {
+			x[i] *= shiftRate
+		}
+
+		Rotate(x, mr)
+
+	case shift && !rotate:
+		Shift(x, os)
+
+		// shrink to the original search range.
+		for i := range x {
+			x[i] *= shiftRate
+		}
+
+	case !shift && rotate:
+		// shrink to the original search range
+		for i := range x {
+			x[i] *= shiftRate
+		}
+
+		Rotate(x, mr)
+
+	case !shift && !rotate:
+		for i := range x {
+			x[i] *= shiftRate
+		}
+	}
+}
+
+// Asy is the asymptotic function.
+func Asy(x []float64, beta float64) {
+	nx := len(x)
+
+	for i := range x {
+		if x[i] > 0 {
+			fi := float64(i)
+			fnx := float64(nx)
+
+			x[i] = math.Pow(x[i], 1+((beta*fi)/(fnx-1)*math.Pow(x[i], 0.5)))
+		}
+	}
+}
+
+// GetMaxFES returns maxFES for the passed dimension, given that it's present
+// in the MaxFES slice, returns -1 otherwise.
+func GetMaxFES(dim int) int {
+	for i, d := range Dimensions {
+		if dim == d {
+			return MaxFES[i]
+		}
+	}
+
+	return -1
+}
+
+// nolint: unused
+func newXopt(n int, mu0 float64) []float64 {
+	gaussDist := &distuv.Normal{
+		Src: rand.NewSource(uint64(time.Now().UnixNano())),
+	}
+
+	tmpvec := make([]float64, n)
+	xopt := make([]float64, n)
+
+	for i := 0; i < n; i++ {
+		tmpvec = append(tmpvec, gaussDist.Rand())
+
+		xopt = append(xopt, 0.5*mu0)
+
+		if tmpvec[i] < 0 {
+			xopt[i] *= -1
+		}
+	}
+
+	return xopt
+}
+
+// newOpt returns a slice of optimal float64 values.
+func newOpt(n int) []float64 {
+	uniformDist := &distuv.Uniform{
+		Src: rand.NewSource(uint64(time.Now().UnixNano())),
+		Min: SearchRange.Min(),
+		Max: SearchRange.Max(),
+	}
+
+	tmpvec := make([]float64, n)
+	xopt := make([]float64, n)
+
+	for i := 0; i < n; i++ {
+		tmpvec = append(tmpvec, uniformDist.Rand())
+
+		xopt = append(xopt, 0.5*uniformDist.Mean())
+
+		if tmpvec[i] < 0 {
+			xopt[i] *= -1
+		}
+	}
+
+	return xopt
+}
+
+// getGnx calculates G_nx.
+func getGnx(p []float64, fnx float64) []float64 {
+	gnx := make([]float64, len(p))
+	gnxsum := 0.0
+
+	for i := 0; i < len(p)-1; i++ {
+		gnx[i] = math.Ceil(p[i] * fnx)
+		gnxsum += gnx[i]
+	}
+
+	gnx[len(p)-1] = fnx - gnxsum
+
+	return gnx
+}
+
+// getMz calculates Mz.
+// nolint: unused
+func getMz(x []float64, z []float64) []float64 {
+	nx := len(x)
+	mz := make([]float64, nx)
+
+	for i := range x {
+		mz = append(mz, x[i]*z[i])
+	}
+
+	return mz
+}
+
+func getMean(x []float64) float64 {
+	mean := stat.Mean(x, nil)
+
+	return mean
+}
+
+func getWeight(x, o []float64, sigma, nx float64) float64 {
+	var sum float64
+
+	for i := range x {
+		sum += math.Pow(x[i]-o[i], 2)
+	}
+
+	// w.
+	return (1 / (math.Sqrt(sum))) * math.Exp(-(sum / 2 * nx * math.Pow(sigma, 2)))
+}

bench/cec2020/inputData.go

@@ -0,0 +1,129 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package cec2020
+
+import (
+	"bufio"
+	"embed"
+	"fmt"
+	"io/fs"
+	"log"
+	"strconv"
+	"strings"
+)
+
+//go:embed input_data/*
+var inputData embed.FS
+
+// LoadInputData loads matrices and shift/rotation/shuffle input data and
+// returns them as maps.
+func LoadInputData(nx int) (map[int][][]float64, map[int][][]float64) {
+	matrices := make(map[int][][]float64)
+	shiftData := make(map[int][][]float64)
+
+	prefix := "input_data"
+
+	// filenames indices.
+	funcNums := []int{1, 2, 3, 7, 4, 16, 6, 22, 24, 25}
+
+	for _, v := range funcNums {
+		fnameMatrix := fmt.Sprintf("%s/M_%d_D%d.txt", prefix, v, nx)
+		fnameShift := fmt.Sprintf("%s/shift_data_%d.txt", prefix, v)
+
+		{
+			matrix, err := inputData.Open(fnameMatrix)
+			if err != nil {
+				log.Printf("matrix: could not read %s, err: %q\n", fnameMatrix, err)
+				continue
+			}
+
+			defer matrix.Close()
+
+			m := parseMatrix(matrix, nx)
+
+			matrices[v] = m
+		}
+
+		{
+			shift, err := inputData.Open(fnameShift)
+			if err != nil {
+				log.Printf("shift: could not read %s, err: %q\n", fnameShift, err)
+				continue
+			}
+
+			defer shift.Close()
+
+			s := parseShiftData(shift, nx)
+
+			shiftData[v] = s
+		}
+	}
+
+	return matrices, shiftData
+}
+
+func parseMatrix(f fs.File, dim int) [][]float64 {
+	if f == nil {
+		log.Fatalln("matrix file is nil, exiting...")
+	}
+
+	var mrows []string
+
+	fMatrix := make([][]float64, 0, dim)
+	scanner := bufio.NewScanner(f)
+
+	for scanner.Scan() {
+		mrows = append(mrows, scanner.Text())
+	}
+
+	for _, r := range mrows {
+		row := strings.Fields(r)
+		flrow := make([]float64, 0, len(row))
+
+		for _, val := range row {
+			fl, err := strconv.ParseFloat(val, 64)
+			if err != nil {
+				log.Fatalf("parseMatrix: could not parse value to float64, val: %s, error: %q, bailing...", val, err)
+			}
+
+			flrow = append(flrow, fl)
+		}
+
+		fMatrix = append(fMatrix, flrow)
+	}
+
+	return fMatrix
+}
+
+func parseShiftData(f fs.File, dim int) [][]float64 {
+	if f == nil {
+		log.Fatalln("shift file is nil, exiting...")
+	}
+
+	shrows := make([]string, 0, dim)
+	fSh := make([][]float64, 0, dim)
+	scanner := bufio.NewScanner(f)
+
+	for scanner.Scan() {
+		shrows = append(shrows, scanner.Text())
+	}
+
+	for _, r := range shrows {
+		row := strings.Fields(r)
+		flrow := make([]float64, 0, len(row))
+
+		for _, col := range row {
+			fl, err := strconv.ParseFloat(col, 64)
+			if err != nil {
+				log.Fatalf("parseShiftData: could not parse value to float64, col: %s, error: %q, bailing...", col, err)
+			}
+
+			flrow = append(flrow, fl)
+		}
+
+		fSh = append(fSh, flrow)
+	}
+
+	return fSh
+}

bench/cec2020/inputData_test.go

@@ -0,0 +1,62 @@
+// Copyright 2023 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package cec2020
+
+import (
+	"testing"
+)
+
+func TestLoadInputData(t *testing.T) {
+	dim := 10
+	fnum := 1
+	m, s := LoadInputData(dim)
+	wantM := -0.6013070130189602
+	gotM := m[fnum][0][0]
+
+	// test matrix data.
+	if wantM != gotM {
+		t.Errorf("unexpected result, want: %v, got: %v", wantM, gotM)
+	}
+
+	wantM = 9.2845625214477390e-01
+	gotM = m[fnum][1][1]
+
+	if wantM != gotM {
+		t.Errorf("unexpected result, want: %v, got: %v", wantM, gotM)
+	}
+
+	// test shiftData.
+	wantS := -70.42955971808618
+	gotS := s[fnum][0][1]
+
+	if wantS != gotS {
+		t.Errorf("unexpected result, want: %v, got: %v", wantS, gotS)
+	}
+
+	wantS = 36.645777950364064
+	gotS = s[fnum][0][10]
+
+	if wantS != gotS {
+		t.Errorf("unexpected result, want: %v, got: %v", wantS, gotS)
+	}
+
+	// test fnum 24 100D.
+	fnum = 24
+	dim = 100
+	m, s = LoadInputData(dim)
+
+	wantM = 0.5743672086265577
+	gotM = m[fnum][613][99]
+
+	if wantM != gotM {
+		t.Errorf("unexpected result, want: %v, got: %v", wantM, gotM)
+	}
+
+	wantS = 73.24860442860452
+	gotS = s[fnum][7][89]
+
+	if wantS != gotS {
+		t.Errorf("unexpected result, want: %v, got: %v", wantS, gotS)
+	}
+}

bench/cec2020/input_data/M_16_D10.txt

@@ -0,0 +1,10 @@
+ -4.3421825269724679e-001  0.0000000000000000e+000 -7.4600765239851408e-001  4.8738590911278667e-001  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000  1.3184106790264916e-001
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+ -7.3438965263097922e-001  0.0000000000000000e+000  5.8388868175915065e-001  2.9036193978534441e-001  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000 -1.8824396231007900e-001
+  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000 -1.2505487099082957e+000  0.0000000000000000e+000 -4.8563196583606894e-001  1.6412165886619889e-001  0.0000000000000000e+000  0.0000000000000000e+000
+  0.0000000000000000e+000 -4.2915364929714345e-002  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000 -9.8381348665932300e-001  0.0000000000000000e+000  0.0000000000000000e+000  1.7398073146235299e-001  0.0000000000000000e+000
+  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000  1.3306980540909588e+000  0.0000000000000000e+000 -9.2141553899407369e-001 -7.3851305250913568e-001  0.0000000000000000e+000  0.0000000000000000e+000
+  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000 -2.8275837277063065e-001  0.0000000000000000e+000  4.5409052882206302e-001 -1.2317631949990260e+000  0.0000000000000000e+000  0.0000000000000000e+000
+  0.0000000000000000e+000  9.1803185617334393e-001  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000  2.9874322727211383e-002  0.0000000000000000e+000  0.0000000000000000e+000  3.9537960985932891e-001  0.0000000000000000e+000
+  4.1406587575637771e-001  0.0000000000000000e+000  7.9605160222132321e-003  5.7249808317331607e-001  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000  0.0000000000000000e+000 -7.0762421205159742e-001

bench/cec2020/input_data/M_16_D15.txt

@@ -0,0 +1,15 @@
+   3.6977522863325879e-01   0.0000000000000000e+00   0.0000000000000000e+00   0.0000000000000000e+00   5.0761948152785419e-01   0.0000000000000000e+00   0.0000000000000000e+00   0.0000000000000000e+00   0.0000000000000000e+00   0.0000000000000000e+00   1.2427817403892079e+00   0.0000000000000000e+00   0.0000000000000000e+00   0.0000000000000000e+00   0.0000000000000000e+00
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bench/cec2020/input_data/M_16_D2.txt

@@ -0,0 +1,2 @@
+ -9.9322397047558009e-001 -1.1621593897879867e-001
+ -1.1621593897879863e-001  9.9322397047558009e-001

bench/cec2020/input_data/M_16_D20.txt

@@ -0,0 +1,20 @@
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bench/cec2020/input_data/M_1_D2.txt

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bench/cec2020/input_data/M_24_D30.txt

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bench/cec2020/input_data/M_2_D10.txt

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bench/cec2020/input_data/M_2_D15.txt

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bench/cec2020/input_data/M_2_D2.txt

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bench/cec2020/input_data/M_2_D20.txt

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bench/cec2020/input_data/M_2_D5.txt

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bench/cec2020/input_data/M_3_D10.txt

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bench/cec2020/input_data/M_3_D15.txt

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bench/cec2020/input_data/M_3_D2.txt

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bench/cec2020/input_data/M_3_D20.txt

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