wanderer/math-optim
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algo: use jDE from the GA package
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leo 2023-02-23 17:56:33 +01:00
parent 7317a023dc
commit 22e2703ed2
Signed by: wanderer
SSH Key Fingerprint: SHA256:Dp8+iwKHSlrMEHzE3bJnPng70I7LEsa3IJXRH/U+idQ
1 changed files with 7 additions and 9 deletions
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16
algo/algo.go
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@ -9,10 +9,10 @@ import (
"sort" "sort"
"sync" "sync"
"git.dotya.ml/wanderer/math-optim/algo/cec2020"
"git.dotya.ml/wanderer/math-optim/algo/de" "git.dotya.ml/wanderer/math-optim/algo/de"
"git.dotya.ml/wanderer/math-optim/algo/ga"
"git.dotya.ml/wanderer/math-optim/bench" "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/report"
"git.dotya.ml/wanderer/math-optim/stats" "git.dotya.ml/wanderer/math-optim/stats"
) )
@ -418,10 +418,8 @@ func DojDE(wg *sync.WaitGroup, m *sync.Mutex) {
func DoCEC2020jDE(wg *sync.WaitGroup, m *sync.Mutex) { func DoCEC2020jDE(wg *sync.WaitGroup, m *sync.Mutex) {
defer wg.Done() defer wg.Done()
cec2020.LogPrintln("starting")
// funcCount is the number of bench functions available and tested. // funcCount is the number of bench functions available and tested.
funcCount := len(c20.Functions) funcCount := len(cec2020.Functions)
// stats for the current algo. // stats for the current algo.
algoStats := make([][]stats.Stats, funcCount) algoStats := make([][]stats.Stats, funcCount)
// ch serves as a way to get the actual computed output. // ch serves as a way to get the actual computed output.
@ -438,7 +436,7 @@ func DoCEC2020jDE(wg *sync.WaitGroup, m *sync.Mutex) {
cr := 0.9 cr := 0.9
for i := range algoStats { for i := range algoStats {
jDE := cec2020.NewjDE() jDE := ga.NewjDE()
// params: // params:
// Generations, minimum bench iterations, mutation strategy, parameter // Generations, minimum bench iterations, mutation strategy, parameter
@ -451,7 +449,7 @@ func DoCEC2020jDE(wg *sync.WaitGroup, m *sync.Mutex) {
// 0..17 to choose a mutation strategy, // 0..17 to choose a mutation strategy,
// 0..1 to select a parameter self-adaptation scheme, // 0..1 to select a parameter self-adaptation scheme,
// np >= 4 as initial population size. // 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() go jDE.Run()
} }
@ -466,8 +464,8 @@ func DoCEC2020jDE(wg *sync.WaitGroup, m *sync.Mutex) {
saveAlgoMeans(*aM) saveAlgoMeans(*aM)
} }
pCh := make(chan report.PicList, funcCount*len(c20.Dimensions)) pCh := make(chan report.PicList, funcCount*len(cec2020.Dimensions))
pMeanCh := make(chan report.PicList, funcCount*len(c20.Dimensions)) pMeanCh := make(chan report.PicList, funcCount*len(cec2020.Dimensions))
for _, algoStat := range algoStats { for _, algoStat := range algoStats {
go plotAllDims(algoStat, "plot", ".pdf", pCh, pMeanCh) go plotAllDims(algoStat, "plot", ".pdf", pCh, pMeanCh)