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Merge branch 'feature-ev' into development

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* feature-ev:
  report/comparisonOfMeans.go: obj init readability
  algo: rm cec2020 package
  report/pics.tmpl: increase pic width
  ga: add pure Differential Evolution
  algo: rm comment [skip ci]
  algo: stop printing pic list when comparing means
  run: handle comparing means for all algos
  run: plug in SOMA T3A
  algo: add DoCEC2020SOMAT3A func
  algo: add PrepCEC2020ComparisonOfMeans+helper func
  algo: use jDE from the GA package
  algo: correct a typo [skip ci]
  ga: improve object/func init readability
  fix(cec2020): add Schwefel Modified's missing case
This commit is contained in:
leo 2023-02-23 23:19:27 +01:00
commit d70b01eee6
Signed by: wanderer
SSH Key Fingerprint: SHA256:Dp8+iwKHSlrMEHzE3bJnPng70I7LEsa3IJXRH/U+idQ
11 changed files with 651 additions and 710 deletions
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193
algo/algo.go
View File

@ -9,20 +9,18 @@ import (
"sort"
"sync"
"git.dotya.ml/wanderer/math-optim/algo/cec2020"
"git.dotya.ml/wanderer/math-optim/algo/de"
"git.dotya.ml/wanderer/math-optim/algo/ga"
"git.dotya.ml/wanderer/math-optim/bench"
c20 "git.dotya.ml/wanderer/math-optim/bench/cec2020"
"git.dotya.ml/wanderer/math-optim/bench/cec2020"
"git.dotya.ml/wanderer/math-optim/report"
"git.dotya.ml/wanderer/math-optim/stats"
)
// var Algos = []string{"Random Search", "Stochastic Hill Climbing"}
// mu protects access to meanStats.
var mu sync.Mutex
// mCoMPL protexts access to comparisonOfMeansPicList.
// mCoMPL protects access to comparisonOfMeansPicList.
var mCoMPL sync.Mutex
var meanStats = &stats.MeanStats{}
@ -146,6 +144,95 @@ func PrepComparisonOfMeans(wg *sync.WaitGroup) (*report.PicList, int) {
return pL, benchCount
}
// PrepCEC2020ComparisonOfMeans prepares for comparison means of CEC2020 algos.
func PrepCEC2020ComparisonOfMeans(wg *sync.WaitGroup) (*report.PicList, int) {
pL := report.NewPicList()
meanStats := GetMeanStats()
algos := getAlgosFromAlgoMeans(meanStats.AlgoMeans)
// construct title consisting of names of all involved algorithms.
for _, v := range algos {
switch pL.Algo {
case "":
pL.Algo = v
default:
pL.Algo += " vs " + v
}
}
log.Println(`generating "Comparison of Means" plots`)
algoCount := len(algos)
dimLen := len(cec2020.Dimensions)
benchCount := len(cec2020.Functions)
for d := 0; d < dimLen; d++ {
// construct comparison for all benchmarking functions.
for i := 0; i < benchCount; i++ {
dimXAlgoMeanVals := make([]stats.AlgoMeanVals, 0, algoCount)
for j := 0; j < algoCount; j++ {
ms := &stats.AlgoMeanVals{
Title: meanStats.AlgoMeans[i+(j*benchCount)].Algo,
MeanVals: meanStats.AlgoMeans[i+(j*benchCount)].BenchMeans[d].MeanVals,
}
dimXAlgoMeanVals = append(dimXAlgoMeanVals, *ms)
}
dimens := meanStats.AlgoMeans[i].BenchMeans[d].Dimens
iterations := meanStats.AlgoMeans[i].BenchMeans[d].Iterations
bench := meanStats.AlgoMeans[i].BenchMeans[d].Bench
wg.Add(1)
// construct plots concurrently.
go PlotMeanValsMulti(
wg, dimens, iterations, bench, "plot-", ".pdf",
dimXAlgoMeanVals...,
)
}
}
// wait for all plotting goroutines.
wg.Wait()
pL.Pics = getComparisonOfMeansPics()
return pL, dimLen
}
// getAlgosFromAlgoMeans extracts algorithms used from the means list and
// returns it as a []string.
func getAlgosFromAlgoMeans(s []stats.AlgoBenchMean) []string {
algos := make([]string, 0)
// learn how many algos were processed based on the data.
for _, v := range s {
// if algos is empty just add the value directly, else determine if
// it's already been added or not.
if len(algos) > 0 {
alreadyadded := false
for _, algoName := range algos {
if algoName == v.Algo {
// early bail if already added.
alreadyadded = true
break
}
}
if !alreadyadded {
algos = append(algos, v.Algo)
}
} else {
algos = append(algos, v.Algo)
}
}
return algos
}
// DoRandomSearch executes a search using the 'Random search' method.
func DoRandomSearch(wg *sync.WaitGroup, m *sync.Mutex) {
defer wg.Done()
@ -418,10 +505,8 @@ func DojDE(wg *sync.WaitGroup, m *sync.Mutex) {
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)
funcCount := len(cec2020.Functions)
// stats for the current algo.
algoStats := make([][]stats.Stats, funcCount)
// ch serves as a way to get the actual computed output.
@ -438,7 +523,7 @@ func DoCEC2020jDE(wg *sync.WaitGroup, m *sync.Mutex) {
cr := 0.9
for i := range algoStats {
jDE := cec2020.NewjDE()
jDE := ga.NewjDE()
// params:
// Generations, minimum bench iterations, mutation strategy, parameter
@ -451,7 +536,7 @@ func DoCEC2020jDE(wg *sync.WaitGroup, m *sync.Mutex) {
// 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)
jDE.Init(-1, 30, 0, 0, np, f, cr, cec2020.Dimensions, cec2020.FuncNames[i], ch, chAlgoMeans)
go jDE.Run()
}
@ -466,8 +551,8 @@ func DoCEC2020jDE(wg *sync.WaitGroup, m *sync.Mutex) {
saveAlgoMeans(*aM)
}
pCh := make(chan report.PicList, funcCount*len(c20.Dimensions))
pMeanCh := make(chan report.PicList, funcCount*len(c20.Dimensions))
pCh := make(chan report.PicList, funcCount*len(cec2020.Dimensions))
pMeanCh := make(chan report.PicList, funcCount*len(cec2020.Dimensions))
for _, algoStat := range algoStats {
go plotAllDims(algoStat, "plot", ".pdf", pCh, pMeanCh)
@ -493,3 +578,87 @@ func DoCEC2020jDE(wg *sync.WaitGroup, m *sync.Mutex) {
stats.SaveTable(algoName, algoStats)
m.Unlock()
}
// DoCEC2020SOMAT3A performs a search using the SOMA T3A method.
func DoCEC2020SOMAT3A(wg *sync.WaitGroup, m *sync.Mutex) {
defer wg.Done()
// funcCount is the number of bench functions available and tested.
funcCount := len(cec2020.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, funcCount)
defer close(ch)
defer close(chAlgoMeans)
// somat3a params.
np := 50
k := 10
mSize := 10
n := 5
njumps := 7
for i := range algoStats {
somat3a := ga.NewSOMAT3A()
// params:
// Generations, minimum bench iterations, initial population size,
// leader candidates, migration candidates group size, number of
// migrants, number of jumps each migrant performs, dimensions, bench
// name and synchronisation channels.
//
// -1 to disable generation limits,
// n > 0 for minimum bench iterations,
// np >= k+mSize as initial population size,
// k, mSize, n and njumps >= 0.
err := somat3a.Init(-1, 30, np, k, mSize, n, njumps,
cec2020.Dimensions, cec2020.FuncNames[i],
ch, chAlgoMeans,
)
if err != nil {
log.Panicf("Failed to initialise SOMA T3A, error: %q", err)
}
go somat3a.Run()
}
// get results.
for i := range algoStats {
s := <-ch
aM := <-chAlgoMeans
algoStats[i] = s
saveAlgoMeans(*aM)
}
pCh := make(chan report.PicList, funcCount*len(cec2020.Dimensions))
pMeanCh := make(chan report.PicList, funcCount*len(cec2020.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 := "SOMA T3A"
// protect access to shared data.
m.Lock()
report.SavePicsToFile(pLs, pLsMean, algoName)
stats.SaveTable(algoName, algoStats)
m.Unlock()
}

5
algo/cec2020/doc.go
View File

@ -1,5 +0,0 @@
// 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

500
algo/cec2020/jDE.go
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@ -1,500 +0,0 @@
// 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...)
}

12
algo/cec2020/log.go
View File

@ -1,12 +0,0 @@
// 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)

167
algo/cec2020/population.go
View File

@ -1,167 +0,0 @@
// 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
}

411
algo/ga/de.go Normal file
View File

@ -0,0 +1,411 @@
// 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"
"git.dotya.ml/wanderer/math-optim/stats"
"gonum.org/v1/gonum/stat/distuv"
)
// DE is a holder for the settings of an instance of a Differential Evolution
// (DE) algorithm.
type DE 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
// DE algorithm (0..17), see mutationStrategies.go for more details.
MutationStrategy 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 (
// MinNPDE is the minimum size of the initial population for DE.
minNPDE = 4
// fMin is the minimum allowed value of the differential weight.
fMinDE = 0.5
// fMax is the maximum allowed value of the differential weight.
fMaxDE = 2.0
// crMin is the minimum allowed value of the crossover probability constant.
crMinDE = 0.2
// crMax is the maximum allowed value of the crossover probability constant.
crMaxDE = 0.9
)
// dELogger is a "custom" DE logger.
var dELogger = newLogger(" *** δ DE:")
// Init initialises the DE algorithm, performs sanity checks on the inputs.
func (d *DE) Init(
generations, benchMinIters, mutStrategy, np int,
f, cr float64,
dimensions []int,
bench string,
ch chan []stats.Stats,
chAlgoMeans chan *stats.AlgoBenchMean,
) {
if d == nil {
dELogger.Fatalln("DE needs to be initialised before calling Run(), exiting...")
}
// check input parameters.
switch {
case generations == 0:
dELogger.Fatalln("Generations cannot be 0, got", generations)
case generations == -1:
dELogger.Println("Generations is '-1', disabling generation limits..")
case benchMinIters < 1:
dELogger.Fatalln("Minimum bench iterations cannot be less than 1, got:", benchMinIters)
case mutStrategy < 0 || mutStrategy > 17:
dELogger.Fatalln("Mutation strategy needs to be from the interval <0; 17>, got", mutStrategy)
case np < minNPDE:
dELogger.Fatalf("NP cannot be less than %d, got: %d\n.", minNPDE, np)
case f < fMinDE || f > fMaxDE:
dELogger.Fatalf("F needs to be from the interval <%f;%f>, got: %f\n.", fMinDE, fMaxDE, f)
case cr < crMinDE || cr > crMaxDE:
dELogger.Fatalf("CR needs to be from the interval <%f;>%f, got: %f\n.", crMinDE, crMaxDE, cr)
case len(dimensions) == 0:
dELogger.Fatalf("Dimensions cannot be empty, got: %+v\n", dimensions)
case bench == "":
dELogger.Fatalln("Bench cannot be unset, got:", bench)
}
d.Generations = generations
d.BenchMinIters = benchMinIters
d.MutationStrategy = mutStrategy
d.NP = np
d.F = f
d.CR = cr
d.Dimensions = dimensions
d.BenchName = bench
d.ch = ch
d.chAlgoMeans = chAlgoMeans
d.initialised = true
}
// InitAndRun initialises the DE algorithm, performs sanity checks on the
// inputs and calls the Run method.
func (d *DE) InitAndRun(
generations, benchMinIters, mutStrategy, np int,
f, cr float64,
dimensions []int,
bench string,
ch chan []stats.Stats,
chAlgoMeans chan *stats.AlgoBenchMean,
) {
if d == nil {
dELogger.Fatalln("DE is nil, NewDE() needs to be called first. exiting...")
}
d.Init(
generations, benchMinIters, mutStrategy, np,
f, cr,
dimensions,
bench,
ch,
chAlgoMeans,
)
d.Run()
}
// Run self-adapting differential evolution algorithm.
func (d *DE) Run() {
if d == nil {
dELogger.Fatalln("DE is nil, NewDE() needs to be called first. exiting...")
}
if !d.initialised {
dELogger.Fatalln("DE needs to be initialised before calling Run(), exiting...")
}
var dEStats []stats.Stats
dEMeans := &stats.AlgoBenchMean{
Algo: "DE",
BenchMeans: make([]stats.BenchMean, 0, len(d.Dimensions)),
}
// run evolve for for all dimensions.
for _, dim := range d.Dimensions {
maxFES := bench.GetGAMaxFES(dim)
fesPerIter := int(float64(maxFES / d.NP))
dEStatDimX := &stats.Stats{
Algo: "DE",
Dimens: dim,
Iterations: d.BenchMinIters,
Generations: maxFES,
NP: d.NP,
F: d.F,
CR: d.CR,
}
funcStats := &stats.FuncStats{BenchName: d.BenchName}
dimXMean := &stats.BenchMean{
Bench: d.BenchName,
Dimens: dim,
Iterations: d.BenchMinIters,
Generations: maxFES,
Neighbours: -1,
}
benchFuncParams := bench.FunctionParams[d.BenchName]
uniDist := distuv.Uniform{
Min: benchFuncParams.Min(),
Max: benchFuncParams.Max(),
}
funcStats.BenchResults = make([]stats.BenchRound, d.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 < d.BenchMinIters; iter++ {
dELogger.Printf("run: %d, bench: %s, %dD, started at %s",
iter, d.BenchName, dim, start,
)
funcStats.BenchResults[iter].Iteration = iter
uniDist.Src = src
// create a population with known params.
pop := newPopulation(d.BenchName, d.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 := d.evaluate(pop)
// distinguish the first or any of the subsequent iterations.
switch i {
case 0:
bestResult = r
default:
// call evolve where actual DE runs.
d.evolve(pop, &uniDist)
// take measurements of current population fitness.
r = d.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 < d.NP-1; x++ {
funcStats.BenchResults[iter].Results = append(
funcStats.BenchResults[iter].Results,
bestResult,
)
}
}
}
elapsed := time.Since(start)
dELogger.Printf("completed: bench: %s, %dD, computing took %s\n",
d.BenchName, dim, elapsed,
)
// get mean vals.
dimXMean.MeanVals = stats.GetMeanVals(funcStats.BenchResults, maxFES)
funcStats.MeanVals = dimXMean.MeanVals
dEStatDimX.BenchFuncStats = append(
dEStatDimX.BenchFuncStats, *funcStats,
)
dEStats = append(dEStats, *dEStatDimX)
dEMeans.BenchMeans = append(dEMeans.BenchMeans, *dimXMean)
}
sort.Sort(dEMeans)
d.chAlgoMeans <- dEMeans
d.ch <- dEStats
}
// evaluate evaluates the fitness of current population.
func (d *DE) 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 DE (Differential Evolution)
// algorithm on the passed population until termination conditions are met.
// nolint: gocognit
func (d *DE) 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] + (d.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 < d.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
}
}
}
// NewDE returns a pointer to a new, uninitialised DE instance.
func NewDE() *DE {
return &DE{}
}

38
algo/ga/jde.go
View File

@ -76,7 +76,14 @@ const (
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) {
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...")
}
@ -124,8 +131,16 @@ func (j *JDE) Init(generations, benchMinIters, mutStrategy, adptScheme, np int,
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.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
@ -139,12 +154,25 @@ func (j *JDE) Init(generations, benchMinIters, mutStrategy, adptScheme, np int,
// 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) {
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.Init(
generations, benchMinIters, mutStrategy, adptScheme, np,
f, cr,
dimensions,
bench,
ch, chAlgoMeans,
)
j.Run()
}

1
bench/cec2020/basicFunctions.go
View File

@ -153,6 +153,7 @@ func SchwefelModified(x []float64) float64 {
case zi < -500:
// g(zi)
sum += (math.Mod(math.Abs(zi), 500)-500)*math.Sin(math.Sqrt(math.Abs(math.Mod(math.Abs(zi), 500)-500))) - (math.Pow(zi-500, 2) - 10000*fnx)
case math.Abs(zi) <= 500:
// g(zi)

7
report/comparisonOfMeans.go
View File

@ -35,9 +35,10 @@ func SaveComparisonOfMeans(p PicList, benchCount int) {
// split the slice to smaller, per-bench slices.
for i := 0; i < len(p.Pics); i += benchCount {
pL := &PicList{}
pL.Pics = p.Pics[i : i+benchCount]
pL.Bench = p.Pics[i].Bench
pL := &PicList{
Pics: p.Pics[i : i+benchCount],
Bench: p.Pics[i].Bench,
}
benchPicLists = append(benchPicLists, *pL)
}

4
report/pics.tmpl
View File

@ -11,7 +11,7 @@
\begin{figure}[h!]
\centering
{{- range $i, $v := .Pics }}
\begin{subfigure}{0.30\textwidth}
\begin{subfigure}{0.46\textwidth}
% note: this accomodates 3 plots a row comfortably..should the requirements
% change, this would have to be reworked.
% {\includesvg[scale=0.45]{ {{- printf "%s" $v.FilePath -}} }}
@ -24,7 +24,7 @@
{{- end -}}
% \newline
{{ range $k, $w := .PicsMean }}
\begin{subfigure}{0.30\textwidth}
\begin{subfigure}{0.46\textwidth}
\vspace{2em}
% {\includesvg[scale=0.45]{ {{- printf "%s" $w.FilePath -}} }}
% using .pdf

21
run.go
View File

@ -26,6 +26,7 @@ var (
jDE = flag.Bool("jde", false, "run Differential Evolution algorithm with parameter self adaptation")
// run CEC2020 jDE by default.
c2jDE = flag.Bool("c2jde", true, "run CEC2020 version of the Differential Evolution algorithm with parameter self adaptation")
c2SOMAT3A = flag.Bool("c2somat3a", false, "run CEC2020 version of the SOMA Team-to-Team Adaptive (T3A)")
)
func run() {
@ -34,7 +35,7 @@ func run() {
flag.Parse()
if *generate {
if !*jDE && !*c2jDE && !*sHC && !*rS {
if !*jDE && !*c2jDE && !*c2SOMAT3A && !*sHC && !*rS {
log.Println("at least one algo needs to be specified, exiting...")
return
@ -56,6 +57,12 @@ func run() {
go algo.DoCEC2020jDE(&wg, &m)
}
if *c2SOMAT3A {
wg.Add(1)
go algo.DoCEC2020SOMAT3A(&wg, &m)
}
if *rS {
wg.Add(1)
@ -74,9 +81,17 @@ func run() {
wg.Wait()
// pL, benchCount := algo.PrepComparisonOfMeans(&wg)
var pL *report.PicList
// report.SaveComparisonOfMeans(*pL, benchCount)
var benchCount int
if *c2jDE && *c2SOMAT3A {
pL, benchCount = algo.PrepCEC2020ComparisonOfMeans(&wg)
} else {
pL, benchCount = algo.PrepComparisonOfMeans(&wg)
}
report.SaveComparisonOfMeans(*pL, benchCount)
report.SaveTexAllPics()
report.SaveTexAllTables()
}