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@ -1,5 +0,0 @@
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// Copyright 2023 wanderer <a_mirre at utb dot cz>
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// SPDX-License-Identifier: GPL-3.0-or-later
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// Package cec2020 implements algorithms required by CEC2020: jDE and T3A.
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package cec2020
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@ -1,500 +0,0 @@
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// Copyright 2023 wanderer <a_mirre at utb dot cz>
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// SPDX-License-Identifier: GPL-3.0-or-later
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package cec2020
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import (
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"sort"
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"time"
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"golang.org/x/exp/rand"
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"git.dotya.ml/wanderer/math-optim/bench/cec2020"
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"git.dotya.ml/wanderer/math-optim/stats"
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"gonum.org/v1/gonum/stat/distuv"
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)
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// JDE is a holder for the settings of an instance of a self-adapting
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// differential evolution (jDE) algorithm.
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type JDE struct {
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// Generations denotes the number of generations the population evolves
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// for. Special value -1 disables limiting the number of generations.
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Generations int
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// BenchMinIters is the number of iterations the bench function will be re-run (for statistical purposes).
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BenchMinIters int
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// Dimensions to solve the problem for.
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Dimensions []int
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// F is the initial value of the differential weight (mutation/weighting factor).
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F float64
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// CR is the initial value of the crossover probability constant.
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CR float64
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// rngF is a random number generator for differential weight (mutation/weighting factor).
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rngF distuv.Uniform
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// cr is a random number generator for crossover probability constant adapted over time.
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rngCR distuv.Uniform
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// MutationStrategy selects the mutation strategy, i.e. the variant of the
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// jDE algorithm (0..17), see mutationStrategies.go for more details.
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MutationStrategy int
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// AdptScheme is the parameter self-adaptation scheme (0..1).
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AdptScheme int
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// NP is the initial population size.
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NP int
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// BenchName is a name of the problem to optimise.
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BenchName string
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// ch is a channel for writing back computed results.
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ch chan []stats.Stats
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// chAlgoMeans is a channel for writing back algo means.
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chAlgoMeans chan *stats.AlgoBenchMean
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// initialised denotes the initialisation state of the struct.
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initialised bool
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}
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const (
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// jDEMinNP is the minimum size of the initial population for jDE.
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// for jDE PaGMO specifies 8.
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jDEMinNP = 4
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// fMin is the minimum allowed value of the differential weight.
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fMin = 0.36
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// fMax is the maximum allowed value of the differential weight.
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fMax = 1.0
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// crMin is the minimum allowed value of the crossover probability constant.
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crMin = 0.0
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// crMax is the maximum allowed value of the crossover probability constant.
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crMax = 1.0
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// tau1 is used in parameter self-adaptation.
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tau1 = 0.1
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// tau2 is used in parameter self-adaptation.
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tau2 = 0.1
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// fl is used in parameter self-adaptation.
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fl = 0.1
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// fu is used in parameter self-adaptation.
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fu = 0.9
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)
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// Init initialises the jDE algorithm, performs sanity checks on the inputs.
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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) {
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if j == nil {
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cec2020Logger.Fatalln("jDE needs to be initialised before calling RunjDE, exiting...")
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}
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// check input parameters.
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switch {
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case generations == 0:
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cec2020Logger.Fatalln("Generations cannot be 0, got", generations)
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case generations == -1:
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cec2020Logger.Println("Generations is '-1', disabling generation limits..")
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case benchMinIters < 1:
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cec2020Logger.Fatalln("Minimum bench iterations cannot be less than 1, got:", benchMinIters)
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case mutStrategy < 0 || mutStrategy > 17:
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cec2020Logger.Fatalln("Mutation strategy needs to be from the interval <0; 17>, got", mutStrategy)
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case adptScheme < 0 || adptScheme > 1:
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cec2020Logger.Fatalln("Parameter self-adaptation scheme needs to be from the interval <0; 1>, got", adptScheme)
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case np < jDEMinNP:
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cec2020Logger.Fatalf("NP cannot be less than %d, got: %d\n.", jDEMinNP, np)
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case f < fMin || f > fMax:
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cec2020Logger.Fatalf("F needs to be from the interval <%f;%f>, got: %f\n.", fMin, fMax, f)
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case cr < crMin || cr > crMax:
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cec2020Logger.Fatalf("CR needs to be from the interval <%f;>%f, got: %f\n.", crMin, crMax, cr)
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case len(dimensions) == 0:
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cec2020Logger.Fatalf("Dimensions cannot be empty, got: %+v\n", dimensions)
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case bench == "":
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cec2020Logger.Fatalln("Bench cannot be empty, got:", bench)
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}
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j.Generations = generations
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j.BenchMinIters = benchMinIters
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j.MutationStrategy = mutStrategy
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j.AdptScheme = adptScheme
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j.NP = np
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j.F = f
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j.CR = cr
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rngsrc := rand.NewSource(uint64(rand.Int63()))
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j.rngF = distuv.Uniform{Min: fMin, Max: fMax, Src: rngsrc}
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j.rngCR = distuv.Uniform{Min: crMin, Max: crMax, Src: rngsrc}
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j.Dimensions = dimensions
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j.BenchName = bench
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j.ch = ch
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j.chAlgoMeans = chAlgoMeans
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j.initialised = true
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cec2020Logger.Printf("jDE init done, jDE:%+v", j)
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}
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// InitAndRun initialises the jDE algorithm, performs sanity checks on the
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// inputs and calls the Run method.
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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) {
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if j == nil {
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cec2020Logger.Fatalln("jDE is nil, NewjDE() needs to be called first. exiting...")
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}
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j.Init(generations, benchMinIters, mutStrategy, adptScheme, np, f, cr, dimensions, bench, ch, chAlgoMeans)
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j.Run()
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}
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// Run self-adapting differential evolution algorithm.
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func (j *JDE) Run() {
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if j == nil {
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cec2020Logger.Fatalln("jDE is nil, NewjDE() needs to be called first. exiting...")
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}
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if !j.initialised {
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cec2020Logger.Fatalln("jDE needs to be initialised before calling Run(), exiting...")
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}
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var jDEStats []stats.Stats
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jDEMeans := &stats.AlgoBenchMean{
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Algo: "jDE",
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BenchMeans: make([]stats.BenchMean, 0, len(j.Dimensions)),
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}
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// run evolve for all dimensions.
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for _, dim := range j.Dimensions {
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maxFES := cec2020.GetMaxFES(dim)
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fesPerIter := int(float64(maxFES / j.NP))
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jDEStatDimX := &stats.Stats{
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Algo: "jDE",
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Dimens: dim,
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Iterations: j.BenchMinIters,
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Generations: maxFES,
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NP: j.NP,
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F: j.F,
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CR: j.CR,
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}
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funcStats := &stats.FuncStats{BenchName: j.BenchName}
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dimXMean := &stats.BenchMean{
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Bench: j.BenchName,
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Dimens: dim,
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Iterations: j.BenchMinIters,
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Generations: maxFES,
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Neighbours: -1,
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}
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uniDist := distuv.Uniform{
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Min: cec2020.SearchRange.Min(),
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Max: cec2020.SearchRange.Max(),
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}
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if maxFES == -1 {
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cec2020Logger.Fatalf("could not get maxFES for current dim (%d), bailing", dim)
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}
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cec2020Logger.Printf("running bench \"%s\" for %dD, maxFES: %d\n",
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j.BenchName, dim, maxFES,
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)
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funcStats.BenchResults = make([]stats.BenchRound, j.BenchMinIters)
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// rand.Seed(uint64(time.Now().UnixNano()))
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// create and seed a source of pseudo-randomness
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src := rand.NewSource(uint64(rand.Int63()))
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// track execution duration.
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start := time.Now()
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for iter := 0; iter < j.BenchMinIters; iter++ {
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cec2020Logger.Printf("run: %d, bench: %s, %dD, started at %s",
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iter, j.BenchName, dim, start,
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)
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funcStats.BenchResults[iter].Iteration = iter
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uniDist.Src = src
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// create a population with known params.
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pop := newPopulation(j.BenchName, j.NP, dim)
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// set population seed.
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pop.Seed = uint64(time.Now().UnixNano())
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// initialise the population.
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pop.Init()
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var bestResult float64
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// the core.
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for i := 0; i < fesPerIter; i++ {
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r := j.evaluate(pop)
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// distinguish the first or any of the subsequent iterations.
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switch i {
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case 0:
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bestResult = r
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default:
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// call evolve where jDE runs.
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j.evolve(pop, &uniDist)
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// take measurements of current population fitness.
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r = j.evaluate(pop)
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// save if better.
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if r < bestResult {
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bestResult = r
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}
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}
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funcStats.BenchResults[iter].Results = append(
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funcStats.BenchResults[iter].Results,
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bestResult,
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)
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// this block makes sure we properly count func evaluations for
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// the purpose of correctly comparable plot comparison. i.e.
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// append the winning (current best) value NP-1 (the first best
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// is already saved at this point) times to represent the fact
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// that while evaluating (and comparing) other population
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// individuals to the current best value is taking place in the
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// background, the current best value itself is kept around and
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// symbolically saved as the best of the Generation.
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for x := 0; x < j.NP-1; x++ {
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funcStats.BenchResults[iter].Results = append(
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funcStats.BenchResults[iter].Results,
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bestResult,
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)
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}
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}
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}
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elapsed := time.Since(start)
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cec2020Logger.Printf("completed: bench: %s, %dD, computing took %s\n",
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j.BenchName, dim, elapsed,
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)
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// get mean vals.
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dimXMean.MeanVals = stats.GetMeanVals(funcStats.BenchResults, maxFES)
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funcStats.MeanVals = dimXMean.MeanVals
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jDEStatDimX.BenchFuncStats = append(
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jDEStatDimX.BenchFuncStats, *funcStats,
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)
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jDEStats = append(jDEStats, *jDEStatDimX)
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jDEMeans.BenchMeans = append(jDEMeans.BenchMeans, *dimXMean)
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}
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sort.Sort(jDEMeans)
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j.chAlgoMeans <- jDEMeans
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j.ch <- jDEStats
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}
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// evaluate evaluates the fitness of current population.
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func (j *JDE) evaluate(pop *Population) float64 {
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f := cec2020.Functions[pop.Problem]
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bestIndividual := pop.Population[pop.GetBestIdx()].CurX
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bestSolution := f(bestIndividual)
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return bestSolution
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}
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// evolve evolves a population by running the jDE (self-adapting Differential
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// Evolution) algorithm on the passed population until termination conditions
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// are met.
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// nolint: gocognit
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func (j *JDE) evolve(pop *Population, uniDist *distuv.Uniform) {
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popCount := len(pop.Population)
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for i, currentIndividual := range pop.Population {
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idcs := make([]int, 0, popCount-1)
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// idcs := make([]int, popCount-1)
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// gather indices.
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for k := 0; k < popCount; k++ {
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if k != i {
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idcs = append(idcs, k)
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}
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}
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// randomly choose 3 of those idcs.
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selectedIdcs := make([]int, 0)
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selectedA := false
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selectedB := false
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selectedC := false
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for !selectedA {
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candidateA := rand.Intn(len(idcs)) % len(idcs)
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if candidateA != i {
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selectedIdcs = append(selectedIdcs, candidateA)
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selectedA = true
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}
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}
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for !selectedB {
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a := selectedIdcs[0]
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candidateB := rand.Intn(len(idcs)) % len(idcs)
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if candidateB != i && candidateB != a {
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selectedIdcs = append(selectedIdcs, candidateB)
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selectedB = true
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}
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}
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for !selectedC {
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a := selectedIdcs[0]
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b := selectedIdcs[1]
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candidateC := rand.Intn(len(idcs)) % len(idcs)
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if candidateC != i && candidateC != a && candidateC != b {
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selectedIdcs = append(selectedIdcs, candidateC)
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selectedC = true
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}
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}
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// selected contains the selected population individuals.
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selected := make([]PopulationIndividual, 0)
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// select individuals for rand/1/bin
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for _, idx := range selectedIdcs {
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for k := 0; k < popCount; k++ {
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if k == idx {
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selected = append(selected, pop.Population[idx])
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}
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}
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}
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mutant := make([]float64, pop.Dimen)
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// mutate.
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for k := 0; k < pop.Dimen; k++ {
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// mutant = a + mut * (b - c)
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mutant[k] = selected[0].CurX[k] + (pop.BestF * (selected[1].CurX[k] - selected[2].CurX[k]))
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// clip values to <0;1>.
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switch {
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case mutant[k] < 0:
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mutant[k] = 0
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case mutant[k] > 1:
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mutant[k] = 1
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}
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}
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crossPoints := make([]bool, pop.Dimen)
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// prepare crossover points (binomial crossover).
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for k := 0; k < pop.Dimen; k++ {
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if v := uniDist.Rand(); v < pop.BestCR {
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crossPoints[k] = true
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} else {
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crossPoints[k] = false
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}
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}
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trial := make([]float64, pop.Dimen)
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// recombine using crossover points.
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for k := 0; k < pop.Dimen; k++ {
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if crossPoints[k] {
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trial[k] = currentIndividual.CurX[k]
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} else {
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trial[k] = mutant[k]
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}
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}
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f := cec2020.Functions[pop.Problem]
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currentFitness := f(currentIndividual.CurX)
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trialFitness := f(trial)
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// replace if better.
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if trialFitness < currentFitness {
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pop.Population[i].CurX = trial
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}
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}
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// adapt parameters.
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j.adaptParameters(pop)
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}
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// adptParameters adapts parameters F and CR based on
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// https://labraj.feri.um.si/images/0/05/CEC09_slides_Brest.pdf.
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func (j *JDE) adaptParameters(p *Population) {
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var nuF float64
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var nuCR float64
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switch {
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case j.AdptScheme == 0:
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if rand2 := j.getRandF(); rand2 < tau1 {
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nuF = fl + (j.getRandF() * fu)
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} else {
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nuF = p.f[len(p.f)-1]
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}
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if rand4 := j.getRandCR(); rand4 < tau2 {
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nuCR = j.getRandCR()
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} else {
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nuCR = p.cr[len(p.cr)-1]
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}
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case j.AdptScheme == 1:
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nuF = p.BestF + j.getRandF()*0.5
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nuCR = p.BestCR + j.getRandCR()*0.5
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}
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// sort out F.
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p.f = append(p.f, nuF)
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// sort out CR
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p.cr = append(p.cr, nuCR)
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// update best, if improved.
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switch {
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case nuF < p.BestF:
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p.BestF = nuF
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case nuCR < p.BestCR:
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p.BestCR = nuCR
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}
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}
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// getRandF returns a random value of F for parameter self-adaptation.
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func (j *JDE) getRandF() float64 {
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return j.rngF.Rand()
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}
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// getRandCR returns a random value of CR for parameter self-adaptation.
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func (j *JDE) getRandCR() float64 {
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return j.rngCR.Rand()
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}
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// NewjDE returns a pointer to a new, uninitialised jDE instance.
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func NewjDE() *JDE {
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||||
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...)
|
||||
}
|
||||
@ -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)
|
||||
@ -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
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user