GA.rar_GA java_hpg_简单遗传算法

package GA; import java.io.BufferedReader; import java.io.BufferedWriter; import java.io.File; import java.io.FileReader; import java.io.FileWriter; import java.util.ArrayList; public class GA { private int POPSIZE = 50; /* 初始种群大小 */ private int MAXGENES = 1000; /* 迭代运算次数 */ private int NVARS = 3; /* 优化参数个数 */ private double PXOVER = 0.80; /* 交叉概率 */ private double PMUTATION = 0.15; /* 突变概率 */ public void SetPopsize(int x) { POPSIZE = x; } public void SetMaxgenes(int x) { MAXGENES = x; } public void SetNvars(int x) { NVARS = x; } public void SetPxover(double x) { PXOVER = x; } public void SetPmutation(double x) { PMUTATION = x; } private int generation; private int cur_best; File outputfile = new File("e:/MyApp/mydata/迭代过程.txt"); private class genotype { double[] gene = new double[NVARS]; double[] upper = new double[NVARS]; double[] lower = new double[NVARS]; double fitness,rfitness,cfitness; } genotype[] population = new genotype[POPSIZE+1]; genotype[] newpopulation = new genotype[POPSIZE+1]; public static void main(String[] args) { GA ga = new GA (); ga.generation = 0; ga.initialize(); ga.evaluate(); ga.keep_the_best(); while(ga.generation<ga.MAXGENES) { ga.generation++; ga.select(); ga.crossover(); ga.mutate(); ga.report(); ga.evaluate(); ga.elitist(); } } public void initialize() { ArrayList <String> mylist = new ArrayList <String> (); try { File myfile = new File("e:/MyApp/mydata/参数取值上下限.txt"); FileReader filereader = new FileReader(myfile); BufferedReader reader = new BufferedReader(filereader); String line = null; while((line = reader.readLine())!= null) { mylist.add(line); } reader.close(); } catch(Exception ex) { ex.printStackTrace(); } double[][] VarRange = new double[NVARS][2]; for(int i=0;i<NVARS;i++) { String[] result = mylist.get(i).split("[\\p{Space}]+"); for(int j=0;j<2;j++) { VarRange[i][j] = Double.parseDouble(result[j]); } } /*数组population与newpopulation中带有引用变量，创建genotype对象*/ for(int n=0;n<POPSIZE+1;n++) { population[n] = new genotype(); newpopulation[n] = new genotype(); } for(int j = 0; j < POPSIZE; j++) { population[j].fitness = 0.0; population[j].rfitness = 0.0; population[j].cfitness = 0.0; for(int i = 0; i < NVARS; i++) { population[j].lower[i] = VarRange[i][0]; population[j].upper[i] = VarRange[i][1]; population[j].gene[i] = randval(population[j].lower[i],population[j].upper[i]); } } } public void evaluate() { double[] VAR = new double[NVARS+1]; for(int mem=0;mem<POPSIZE;mem++) { for(int i=0;i<NVARS;i++) { VAR[i+1] = population[mem].gene[i]; } population[mem].fitness = VAR[1]*VAR[1]-VAR[1]*VAR[2]+VAR[3]; } } public void keep_the_best() { cur_best = 0; for (int mem = 0; mem < POPSIZE; mem++) { if (population[mem].fitness > population[POPSIZE].fitness) { cur_best = mem; population[POPSIZE].fitness = population[mem].fitness; } } for (int i = 0; i < NVARS; i++) { population[POPSIZE].gene[i] = population[cur_best].gene[i]; } } public void select() { double p,sum = 0; for (int mem = 0; mem < POPSIZE; mem++) { sum = sum + population[mem].fitness; } for (int mem = 0; mem < POPSIZE; mem++) { population[mem].rfitness = population[mem].fitness/sum; } population[0].cfitness = population[0].rfitness; for (int mem = 1; mem < POPSIZE; mem++) { population[mem].cfitness = population[mem-1].cfitness + population[mem].rfitness; } for (int i = 0; i < POPSIZE; i++) { p = Math.random(); if (p < population[0].cfitness) { newpopulation[i] = population[0]; } else { for (int j = 0; j < POPSIZE; j++) { if (p >= population[j].cfitness && p < population[j+1].cfitness) { newpopulation[i] = population[j+1]; } } } } for (int i = 0; i < POPSIZE; i++) { population[i] = newpopulation[i]; } } public void crossover() { double x; int one=0,count=0; for(int mem=0;mem<POPSIZE;mem++) { x = Math.random(); if(x<PXOVER) { count++; if(count%2==0) Xover(one,mem); else one = mem; } } } public void Xover(int one,int two) { int point = (int)(Math.random()*(NVARS-1)+1); for(int i=0;i<point;i++) { double temp = 0; temp = population[one].gene[i]; population[one].gene[i] = population[two].gene[i]; population[two].gene[i] = temp; } } public void mutate() { double lbound,hbound; for (int i = 0; i < POPSIZE; i++) { for (int j = 0; j < NVARS; j++) { double p = Math.random(); if(p < PMUTATION ) { lbound = population[i].lower[j]; hbound = population[i].upper[j]; population[i].gene[j] = randval(lbound,hbound); } } } } public void elitist() { int min_mem,max_mem; double min_fitness,max_fitness; min_mem=0; min_fitness=population[0].fitness; max_mem=0; max_fitness=population[0].fitness; for (int i = 0; i < POPSIZE - 1; i++) { if(population[i].fitness < population[i+1].fitness) { if (population[i].fitness <= min_fitness) { min_fitness = population[i].fitness; min_mem = i; } if (population[i+1].fitness >= max_fitness) { max_fitness = population[i+1].fitness; max_mem = i + 1; } } else { if (population[i].fitness >= max_fitness) { max_fitness = population[i].fitness; max_mem = i; } if (population[i+1].fitness <= min_fitness) { min_fitness = population[i+1].fitness; min_mem = i + 1; } } } if (max_fitness >= population[POPSIZE].fitness) { for (int i = 0; i < NVARS; i++) { population[POPSIZE].gene[i] = population[max_mem].gene[i]; population[POPSIZE].fitness = population[max_mem].fitness; } } else { for (int i = 0; i < NVARS; i++) { population[min_mem].gene[i] = population[POPSIZE].gene[i]; population[min_mem].fitness = population[POPSIZE].fitness; } } } public void report() { try { FileWriter filewriter = new FileWriter(outputfile,true); BufferedWriter writer = new BufferedWriter(filewriter); writer.write("代数"+"\t"+"变量"+"\t"+"最优取值"+"\r\n"); for(int i=0;i<NVARS;i++) { writer.write(generation+"\t"+(i+1)+"\t"+population[POPSIZE].gene[i]+"\r\n"); }