ga.zip_GA改进_GA无功优化_无功_无功 优化 遗传算法_电力 遗传算法

/*在输入文件中，按以下格式输入 n1 n2 n3 n1为调压发电机数 n2为变比可调变压器数 n3为投切电容器组数 site lbound ubound step 位置 下限 上限 步长 site：变压器填其编号 发电机和电容器填其所连节点号 按发电机、变压器、电容器顺序填写。 */ #include<stdio.h> #include<stdlib.h> #include<math.h> #include<time.h> #define POPSIZE 40 //种群规模 #define MAXGENS 100 //最大繁殖代数 #define NVARS 50 //所需处理的变量数（一般不超过50，在此可以固定为50） #define PC1 0.9 //最大交叉概率 #define PC2 0.8 //最小交叉概率 #define PM1 0.1 //最大变异概率 #define PM2 0.05 //最小变异概率 extern double PowerFlow(); extern void read_data(); extern void outputPowerFlow(); extern double ph,error_max; char FILENAME[50]; char FILENAME1[50]; int generation; //当前繁殖代数 int cur_best; //最佳个体 int seed; double *gav,*gak,*gaq; double fmax,favg; int *gagene,*gatran,*gacap; int num_gagene,num_gatran,num_gacap; int site; int FLAG2; double k1,k2; FILE *galog; //输出文件 struct genotype { double gene[NVARS]; //变量 double fitness; //适应值 double upper[NVARS]; //变量上限 double lower[NVARS]; //变量下限 double step[NVARS]; //变化步长 double rfitness; //相对适应值 double cfitness; //之前适应值之和 }; struct genotype population[POPSIZE+ 1]; struct genotype newpopulation[POPSIZE+ 1]; struct genotype savepopulation[MAXGENS]; int *newSpaceInt(int); double *newSpaceDouble(int); void initialize(void); double randval(double,double,double); void randpopulation(int,int); void evaluate(void); void keep_the_best(void); void elitist(void); void select(void); void crossover(void); void Xover(int,int); void swap(double* ,double* ); void mutate(void); void report(void); void bubble(int); double compare(int ,int); int *newSpaceInt(int n1) { int i; int *a=(int *)calloc(n1,sizeof(int)); if(a==NULL) { printf("\nno memory in a"); exit(0); } for(i=0;i<n1;i++) a[i]=0; return a; } double *newSpaceDouble(int n1) { int i; double *a=(double *)calloc(n1,sizeof(double)); if(a==NULL) { printf("\nno memory in a"); exit(0); } for(i=0;i<n1;i++) a[i]=0.0; return a; } void bubble(int num)//冒泡排序（大数在后） { int i,j; struct genotype temp; for(j=0;j<num-1;j++) { for(i=0;i<num-1-j;i++) { if(population[i].fitness >population[i+1].fitness ) { temp=population[i]; population[i]=population[i+1]; population[i+1] =temp; } } } } void randpopulation(int num1,int num2)//随机产生新个体 { int i,j; for (i = 0; i< num_gagene+num_gatran+num_gacap; i++) for( j = num1; j < num2; j++) population[j].gene[i] = randval(population[j].lower[i], population[j].upper[i],population[j].step[i]); } void initialize(void)//初始化 { FILE *fin; int i,j,k,n; //int *gagene,*gatran,*gacap; double lbound, ubound,step; if ((fin = fopen(FILENAME1, "r")) ==NULL) { printf("打开遗传算法数据文件出错！"); exit(1); } if ((galog = fopen("遗传算法进化结果.dat","w"))==NULL) { printf("生成遗传算法结果文件出错！"); exit(1); } /* initialize variables within the bounds */ fscanf(fin, "%d %d %d",&num_gagene,&num_gatran,&num_gacap); gagene=newSpaceInt(num_gagene); gatran=newSpaceInt(num_gatran); gacap=newSpaceInt(num_gacap); for (i = 0; i< num_gagene+num_gatran+num_gacap; i++) { fscanf(fin, "%d %lf %lf %lf",&site,&ubound,&lbound,&step); //printf("\n%lf",step); if(i>=0&&i<num_gagene) gagene[i]=site; if(i>=num_gagene&&i<num_gagene+num_gatran) gatran[i-num_gagene]=site; if(i>=num_gagene+num_gatran&&i<num_gagene+num_gatran+num_gacap) gacap[i-num_gagene-num_gatran]=site; for( j = 0; j < POPSIZE; j++) { population[j].fitness = 0; population[j].rfitness = 0; population[j].cfitness = 0; population[j].lower[i] =lbound; population[j].upper[i] =ubound; population[j].step[i] =step; population[j].gene[i] = randval(population[j].lower[i], population[j].upper[i],population[j].step[i]); //printf("%d %d %lf ",j,i,population[j].gene[i]); } } n=POPSIZE/2;//竞争选择初始群体 k=0; do { evaluate(); bubble(POPSIZE); for(i=k;i<k+n;i++) savepopulation[i]=population[i+n-k]; randpopulation(0,POPSIZE); k+=n; }while(k<POPSIZE); for(i=0;i<POPSIZE;i++) population[i]=savepopulation[i]; fclose(fin); } /**********************************************************************/ /* Random value generator: Generates a value within bounds */ /**********************************************************************/ double randval(double low, double high ,double step)//随机产生新个体 { double val; val = rand()%(int)((high-low)/step+1)*step+low; return(val); } /********************************************************************/ /* Evaluation function: This takes a user defined function. */ /* Each time this is changed, the code has to be recompiled. */ /*The current function is: x[l]^2-x[1]*x[2]+x[3] */ /********************************************************************/ void evaluate(void)//适应度评估 { int mem; int i; k1=exp(generation); // k1=200000; k2=k1; gav=newSpaceDouble(num_gagene); gak=newSpaceDouble(num_gatran); gaq=newSpaceDouble(num_gacap); for (mem = 0; mem < POPSIZE; mem++) { for (i = 0; i < num_gagene+num_gatran+num_gacap; i++) { if(i>=0&&i<num_gagene) gav[i]=population[mem].gene[i]; if(i>=num_gagene&&i<num_gagene+num_gatran) gak[i-num_gagene]=population[mem].gene[i]; if(i>=num_gagene+num_gatran&&i<num_gagene+num_gatran+num_gacap) gaq[i-num_gagene-num_gatran]=population[mem].gene[i]; } population[mem].fitness=1.0/PowerFlow(); } free(gav); free(gak); free(gaq); favg=0; for (mem = 0; mem <POPSIZE; mem++) favg += population[mem].fitness; favg/=POPSIZE; bubble(POPSIZE); fmax=population[POPSIZE-1].fitness; } void keep_the_best()//选择最优个体 { int i; for(i = 0; i<num_gagene+num_gatran+num_gacap; i++) population[POPSIZE].gene[i] = population[POPSIZE-1].gene[i]; population[POPSIZE].fitness = fmax; } void elitist()//最优个体替换 { int i,k; static int j=1; savepopulation[j].fitness=population[POPSIZE].fitness; if(j>1&&savepopulation[j].fitness==savepopulation[j-1].fitness) { for( k = 1; k <=POPSIZE/5; k++) { for(i=0;i<num_gagene+num_gatran+num_gacap;i++) population[k].gene[i] = randval(population[k].lower[i], population[k].upper[i],population[k].step[i]); } } j++; if(fmax>=population[POPSIZE].fitness) { for(i=0;i<num_gagene+num_gatran+num_gacap;i++) { population[0].gene[i] = population[POPSIZE].gene[i]; population[POPSIZE].gene[i]=population[POPSIZE-1].gene[i]; } population[0].fitness = population[POPSIZE].fitness; population[POPSIZE].fitness= fmax; } else { for(i=0;i<num_gagene+num_gatran+num_gacap;i++) population[0].gene[i] = population[POPSIZE].gene[i]; population[0].fitness = population[POPSIZE].fitness; } } void select(void)//选择 { int i; for(i=0;i<POPSIZE;i++) { int mem1,mem2; mem1=rand()%POPSIZE; mem2=rand()%POPSIZE; if(population[mem