java-GA.rar_GA_genetic_遗传算法 java_遗传算法 java_遗传算法Java

public class GA { final static int N=3; // number of variables final static int M=1; // number of objectives final static int TYPE=1; // 1=max;-1=min final static int GEN=400; // maximum generation number final static int POP_SIZE=30; final static double P_MUTATION=0.15 ; //变异概率 final static double P_CROSSOVER=0.7; //交配概率，当随机数小于该值时，该染色体被选中 static double [][] CHROMOSOME=new double [POP_SIZE+1][N+1]; static double [][]OBJECTIVE=new double [POP_SIZE+1][M+1]; static double []q=new double[POP_SIZE+1]; final static long RAND_MAX=1000000; ///////////////////////////////////// public static double myu(double a, double b) // Uniform Distribution { double y; if(a>b) { System.out.print("\nThe first parameter should be less than the second!"); //exit(1); } y = Math.random(); return (a+(b-a)*y); } public static void objective_function() { double x1,x2,x3; int i; for(i = 1; i <= POP_SIZE; i++) { x1 = CHROMOSOME[i][1]; x2 = CHROMOSOME[i][2]; x3 = CHROMOSOME[i][3]; OBJECTIVE[i][1] = Math.sqrt(x1)+Math.sqrt(x2)+Math.sqrt(x3); } for(i=1;i<=POP_SIZE;i++) OBJECTIVE[i][0] = OBJECTIVE[i][1]; } /////////////////////////// public static int constraint_check(double x[]) { double a; int n; for(n=1;n<=N;n++) if(x[n]<0) return 0; a = x[1]*x[1]+2*x[2]*x[2]+3*x[3]*x[3]; if(a>1) return 0; return 1; } ///////////////////////////////////////// public static void initialization() { double []x=new double [N+1]; // N is the number of variables int i,j; for(i=1; i<=POP_SIZE; i++) { do{ for(j=1; j<=N; j++) x[j]=myu(0,1);} while (constraint_check(x) == 0); for(j=1; j<=N; j++) CHROMOSOME[i][j]=x[j]; } } ////////////////////// public static void main(String arc[]) { int i, j; double a; q[0]=0.05; a=0.05; for(i=1; i<=POP_SIZE; i++) { a=a*0.95; q[i]=q[i-1]+a; } //q[]为seletion()函数中随机选择染色体做准备 initialization(); evaluation(0); java.text.NumberFormat formater = java.text.DecimalFormat.getInstance(); formater.setMaximumFractionDigits(4); for(i=1; i<=GEN; i++) { selection(); crossover(); mutation(); evaluation(i); System.out.print("Generation NO."); System.out.println(i); //System.out.println("x=("); for(j=1; j<=N; j++) { if(j<N) System.out.println(formater.format(CHROMOSOME[0][j])); else System.out.println(formater.format(CHROMOSOME[0][j])); } if(M==1) System.out.println(formater.format(OBJECTIVE[0][1])); else { System.out.println(")\nf=("); for(j=1; j<=M; j++) { if(j<M) System.out.println(formater.format(OBJECTIVE[0][j])); else System.out.println(formater.format(OBJECTIVE[0][j])); } System.out.println(") Aggregating Value=%3.4f\n" + OBJECTIVE[0][0]); } } System.out.println("\n"); // return 1; } //将染色体和目标函数按目标函数值由大到小进行排序 public static void evaluation(int gen) { double a; int i, j, k, label; objective_function(); if(gen==0) { for(k=0; k<=M; k++) OBJECTIVE[0][k]=OBJECTIVE[1][k]; for(j = 1; j <= N; j++) CHROMOSOME[0][j]=CHROMOSOME[1][j]; } for(i=0; i<POP_SIZE; i++) { label=0; a=OBJECTIVE[i][0]; for(j=i+1; j<=POP_SIZE; j++) if((TYPE*a)<(TYPE*OBJECTIVE[j][0])) { a=OBJECTIVE[j][0]; label=j; } if(label!=0) { for(k=0; k<=M; k++) { a=OBJECTIVE[i][k]; OBJECTIVE[i][k]=OBJECTIVE[label][k]; OBJECTIVE[label][k]=a; } for(j=1; j<=N; j++) { a=CHROMOSOME[i][j]; CHROMOSOME[i][j]=CHROMOSOME[label][j]; CHROMOSOME[label][j]=a; } } } } ////////////////////////////////////////// public static void selection() { double r; double [][] temp=new double[POP_SIZE+1][N+1]; int i, j, k; for(i=1; i<=POP_SIZE; i++) { r = myu(0,q[POP_SIZE]); for(j=0; j<=POP_SIZE; j++) { if(r<=q[j]) { for(k=1; k<=N; k++) temp[i][k]=CHROMOSOME[j][k]; break; } } } for(i=1; i<=POP_SIZE; i++) for(k=1; k<=N; k++) CHROMOSOME[i][k]=temp[i][k]; } ///////////////////// 交配 ////////////////////// public static void crossover() { int i, j, jj, k, pop; double r; double[] x = new double[N+1]; double[] y = new double[N+1]; pop=POP_SIZE/2; for(i=1; i<=pop; i++) { if(myu(0,1)>P_CROSSOVER) continue; j=(int)myu(1,POP_SIZE); jj=(int)myu(1,POP_SIZE); r=myu(0,1); for(k=1; k<=N; k++) { x[k]=r*CHROMOSOME[j][k]+(1-r)*CHROMOSOME[jj][k]; y[k]=r*CHROMOSOME[jj][k]+(1-r)*CHROMOSOME[j][k]; } if(constraint_check(x)==1) for(k=1; k<=N; k++) CHROMOSOME[j][k]=x[k]; if(constraint_check(y)==1) for(k=1; k<=N; k++) CHROMOSOME[jj][k]=y[k]; } } ////////////////////////////////////////// public static void mutation() { int i, j, k; double infty; double[] x = new double[N+1]; double[] y = new double[N+1]; double[] direction = new double[N+1]; double INFTY=10, precision=0.0001; for(i=1; i<=POP_SIZE; i++) { if(myu(0,1)>P_MUTATION) continue; for(k=1; k<=N; k++) x[k] = CHROMOSOME[i][k]; for(k=1; k<=N; k++) if(myu(0,1)<0.5) direction[k]=myu(-1,1); else direction[k]=0; infty=myu(0,INFTY); while(infty>precision) { for(j=1; j<=N; j++) y[j]=x[j]+infty*direction[j]; if(constraint_check(y)==1) { for(k=1; k<=N; k++) CHROMOSOME[i][k]=y[k]; break; } infty=myu(0,infty); } } } }