428 lines
12 KiB
Go
428 lines
12 KiB
Go
// 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 ga
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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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// dELogger is a "custom" jDE logger.
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var jDELogger = newLogger(" *** δ jDE:")
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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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jDELogger.Fatalln("jDE needs to be initialised before calling Run(), 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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jDELogger.Fatalln("Generations cannot be 0, got", generations)
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case generations == -1:
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jDELogger.Println("Generations is '-1', disabling generation limits..")
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case benchMinIters < 1:
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jDELogger.Fatalln("Minimum bench iterations cannot be less than 1, got:", benchMinIters)
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case mutStrategy < 0 || mutStrategy > 17:
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jDELogger.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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jDELogger.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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jDELogger.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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jDELogger.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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jDELogger.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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jDELogger.Fatalf("Dimensions cannot be empty, got: %+v\n", dimensions)
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case bench == "":
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jDELogger.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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gaLogger.Printf("jDE init done for bench %s", j.BenchName)
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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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jDELogger.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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jDELogger.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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jDELogger.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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jDELogger.Fatalf("could not get maxFES for current dim (%d), bailing", dim)
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}
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jDELogger.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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jDELogger.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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// uniDist.Src = rand.NewSource(uint64(rand.Int63()))
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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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jDELogger.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 := pop.ProblemFunc
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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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f := pop.ProblemFunc
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genChampIdx := pop.GetBestIdx()
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genChamp := ChampionIndividual{
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X: pop.Population[genChampIdx].CurX,
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}
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genChampFitness := f(genChamp.X)
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for i, currentIndividual := range pop.Population {
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currentFitness := f(currentIndividual.CurX)
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if genChampFitness < f(pop.Champion.X) {
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pop.Champion.X = genChamp.X
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}
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donors := pop.SelectDonors(i)
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mutant := mutate(j.MutationStrategy, currentIndividual, genChamp, pop, donors...)
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if mutant == nil {
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jDELogger.Fatalln("Somehow you managed to pass in an out-of-bounds mutation strategy, don't know what to do, exiting...")
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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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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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p.CurF = nuF
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// sort out CR
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p.cr = append(p.cr, nuCR)
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p.CurCR = 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{}
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}
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