遗传算法-SGA代码实现

#include<stdio.h>#include<string.h>#include<stdlib.h>#include<malloc.h>#include<math.h>/* 全局变量 */struct individual                       /* 个体*/{    unsigned *chrom;                    /* 染色体 */    double   fitness;                   /* 个体适应度*/    double   varible;                   /* 个体对应的变量值*/    int      xsite;                     /* 交叉位置 */    int      parent[2];                 /* 父个体  */    int      *utility;                  /* 特定数据指针变量 */};struct bestever                         /* 最佳个体*/{    unsigned *chrom;                    /* 最佳个体染色体*/    double   fitness;                   /* 最佳个体适应度 */    double   varible;                   /* 最佳个体对应的变量值 */    int      generation;                /* 最佳个体生成代 */}; struct individual *oldpop;             /* 当前代种群 */ struct individual *newpop;             /* 新一代种群 */ struct bestever bestfit;               /* 最佳个体 */ double sumfitness;                     /* 种群中个体适应度累计 */ double max;                            /* 种群中个体最大适应度 */ double avg;                            /* 种群中个体平均适应度 */ double min;                            /* 种群中个体最小适应度 */ float  pcross;                         /* 交叉概率 */ float  pmutation;                      /* 变异概率 */ int    popsize;                        /* 种群大小  */ int    lchrom;                         /* 染色体长度*/ int    chromsize;                      /* 存储一染色体所需字节数 */ int    gen;                            /* 当前世代数 */ int    maxgen;                         /* 最大世代数   */ int    run;                            /* 当前运行次数 */ int    maxruns;                        /* 总运行次数   */ int    printstrings;                   /* 输出染色体编码的判断，0 -- 不输出, 1 -- 输出 */ int    nmutation;                      /* 当前代变异发生次数 */ int    ncross;                         /* 当前代交叉发生次数 *//* 随机数发生器使用的静态变量 */static double oldrand[55];static int jrand;static double rndx2;static int rndcalcflag;/* 输出文件指针 */FILE *outfp ;/* 函数定义 */void advance_random();int flip(float);int rnd(int, int);void randomize();double randomnormaldeviate();float randomperc(),rndreal(float,float);void warmup_random(float);void initialize(),initdata(),initpop();void initreport(),generation(),initmalloc();void freeall(),nomemory(char *),report();void writepop(),writechrom(unsigned *);void preselect();void statistics(struct individual *);void title(),repchar (FILE *,char *,int);void skip(FILE *,int);int select();void objfunc(struct individual *);int crossover (unsigned *, unsigned *, unsigned *, unsigned *);void mutation(unsigned *);void initialize()    /*遗传算法初始化*/{/*键盘输入遗传算法参数*/initdata();/*确定染色体的字节长度*/chromsize = (lchrom/(8*sizeof(unsigned)));if(lchrom%(8*sizeof(unsigned)))chromsize++;/*分配给全局数据结构空间*/initmalloc();/*初始化随机数发生器*/randomize();/*初始化全局计数变量和一些数量*/nmutation =0;ncross = 0;bestfit.fitness = 0.0;bestfit.generation = 0;/*初始化种群，并统计计算结果*/initpop();statistics(oldpop);initreport();}void initdata()   /*遗传算法参数输入*/{char answer[2];printf("种群大小(20-100): ");scanf("%d",&popsize);if((popsize%2) != 0){fprintf(outfp, "种群大小已设置为偶数\n");popsize++;}printf("染色体长度(8-40):");scanf("%d", &lchrom);printf("是否输出染色体编码(y/n):");printstrings = 1;scanf("%s", &answer);if(strncmp(answer,"n",1) == 0)printstrings = 0;printf("最大世代数(100-300):");scanf("%d", &maxgen);printf("交叉率(0.2-0.9):");scanf("%f", &pcross);printf("变异率(0.01-0.1):");scanf("%f",&pmutation);}void initpop()   /*随机初始化种群*/{int j,j1, k,stop;unsigned mask = 1;for(j=0; j<popsize; j++){for(k=0; k< chromsize; k++){oldpop[j].chrom[k] = 0;if(k == (chromsize -1))stop = lchrom - (k*(8*sizeof(unsigned)));elsestop = 8* sizeof(unsigned);for(j1 = 1; j1<= stop; j1++){oldpop[j].chrom[k] = oldpop[j].chrom[k]<<1;if(flip(0.5))      /*是否变异*/oldpop[j].chrom[k] = oldpop[j].chrom[k]|mask;}}oldpop[j].parent[0] = 0 ;  /*初始化个体信息*/oldpop[j].parent[1] = 0;oldpop[j].xsite = 0;objfunc(&(oldpop[j]));      /*计算初始适应度*/}}void initreport()         /*初始参数输出*/{skip(outfp,1);fprintf(outfp,"       基本遗传算法参数\n");fprintf(outfp,"----------------------------------------------------------\n");fprintf(outfp,"     种群大小(popsize)     = %d\n", popsize);fprintf(outfp,"     染色体长度(lchrom)    = %d\n", lchrom);fprintf(outfp,"     最大进化代数(maxgen)  = %d\n", maxgen);fprintf(outfp,"     交叉概率(pcross)      = %f\n", pcross);fprintf(outfp,"     变异概率(pmutation)   = %f\n", pmutation);fprintf(outfp,"----------------------------------------------------------\n");skip(outfp,1);fflush(outfp);}void generation(){int mate1, mate2, jcross, j=0;/*每代运算前进行预选, 即计算sumfitness*/preselect();/*选择，交叉，变异*/do{/*挑选交叉配对*/mate1 = select();mate2 = select();/*交叉和变异*/jcross = crossover(oldpop[mate1].chrom, oldpop[mate2].chrom,newpop[j].chrom,newpop[j+1].chrom);mutation(newpop[j].chrom);mutation(newpop[j+1].chrom);/*解码，计算适应度*/objfunc(&(newpop[j]));/*记录亲子关系和交叉位置*/newpop[j].parent[0] = mate1 +1;newpop[j].xsite = jcross;newpop[j].parent[1] = mate2 +1;objfunc(&(newpop[j+1]));newpop[j+1].parent[0] = mate1 +1;newpop[j+1].xsite = jcross;newpop[j+1].parent[1] = mate2 +1;j+=2;}while(j<(popsize-1));}void initmalloc()   /*为全局数据变量分配空间*/{unsigned nbytes;int j;/*分配给当前代和新一代种群内存空间*/nbytes = popsize*sizeof(struct  individual);if((oldpop = (struct individual *) malloc(nbytes)) == NULL)nomemory("oldpop");if((newpop = (struct individual *) malloc (nbytes)) == NULL)nomemory("newpop");/*分配给染色体内存空间*/nbytes = chromsize* sizeof(unsigned);for(j=0; j< popsize; j++){if((oldpop[j].chrom = (unsigned*)malloc(nbytes)) == NULL)nomemory("oldpop chromosomes");if((newpop[j].chrom = (unsigned*) malloc(nbytes)) == NULL)nomemory("newpop chromsomes");}if((bestfit.chrom = (unsigned *)malloc(nbytes)) == NULL)nomemory("bestfit chromosome");}void freeall()     /*释放内存空间*/{int i;for(i=0; i<popsize; i++){free(oldpop[i].chrom);free(newpop[i].chrom);}free(oldpop);free(newpop);free(bestfit.chrom);}void nomemory(char *string)    /*内存不足，退出*/{fprintf(outfp,"malloc:out of memory makeing %s!\n", string);exit(-1);}void report()   /*输出种群统计结果*/{repchar(outfp,"-",88);skip(outfp,1);if(printstrings == 1){repchar(outfp," ", ((80-17)/2));fprintf(outfp, "模拟计算统计报告\n");fprintf(outfp,"世代数%3d", gen);repchar (outfp," ", (80-28));fprintf(outfp, "世代数 %3d\n", (gen+1));fprintf(outfp,"个体     染色体编码");repchar (outfp, " ",lchrom - 12);fprintf(outfp,"适应度     父个体  交叉位置    ");fprintf(outfp,"染色体编码         ");fprintf(outfp,"适应度\n");repchar (outfp,"-", 88);skip(outfp,1);writepop();repchar(outfp,"-",88);skip(outfp,1);}fprintf(outfp,"第 %d 代数统计: \n",gen);fprintf(outfp,"总交叉操作次数 = %d,总变异操作数 = %d \n",ncross,nmutation);fprintf(outfp,"最小适应度: %f 最大适应度: %f 平均适应度: %f \n", min,max,avg);fprintf(outfp,"迄今发现最佳个体 => 所在代数: %d ", bestfit.generation);fprintf(outfp,"适应度: %f 染色体：", bestfit.fitness);writechrom((&bestfit)->chrom);fprintf(outfp," 对应的变量值: %f ", bestfit.varible);skip(outfp,1);repchar(outfp,"-", 88);skip(outfp,1);}void writepop(){struct individual *pind;int j;for(j=0; j<popsize;j++){fprintf(outfp, "%3d) ", j+1);/*当前代个体*/pind = &(oldpop[j]);writechrom(pind->chrom);fprintf(outfp,"  %8f  ", pind->fitness);/*新一代个体*/pind = &(newpop[j]);fprintf(outfp," (%2d, %2d)    %2d  ",pind->parent[0], pind->parent[1], pind->xsite);writechrom(pind->chrom);fprintf(outfp,"  %8f\n", pind->fitness);}}void writechrom(unsigned *chrom)    /*输出染色体编码*/{int j,k, stop;unsigned mask = 1, tmp;for(k=0; k< chromsize; k++){tmp = chrom[k];if(k==(chromsize-1))stop = lchrom -(k*(8*sizeof(unsigned)));elsestop = 8*sizeof(unsigned );for(j=0; j<stop; j++){if(tmp &mask )fprintf(outfp, "1");elsefprintf(outfp, "0");tmp = tmp >> 1;}}}void  preselect(){int j;sumfitness = 0;for(j=0; j< popsize; j++)sumfitness +=oldpop[j].fitness;}int select()    /*轮盘赌选择*/{extern float randomperc();float sum, pick;int i;pick = randomperc();sum = 0;if(sumfitness != 0){for(i=0; (sum<pick) && (i<popsize); i++)sum += (float)(oldpop[i].fitness/sumfitness);}else i = rnd(1, popsize);return (i-1);}void statistics(struct individual *pop)    /*计算种群统计数据*/{int i, j;sumfitness = 0.0;min = pop[0].fitness;max = pop[0].fitness;/*计算最大，最小和累计适应度*/for(j=0; j< popsize; j++){sumfitness = sumfitness + pop[j].fitness;if(pop[j].fitness > max)max = pop[j].fitness;if(pop[j].fitness < min)min = pop[j].fitness;/*new global best -fit individual */if(pop[j].fitness > bestfit.fitness){for(i=0; i< chromsize; i++)bestfit.chrom[i] = pop[j].chrom[i];bestfit.fitness = pop[j].fitness;bestfit.varible = pop[j].varible;bestfit.generation = gen;}}/*计算平均适应度*/avg = sumfitness/popsize;}void  title(){//printf("基本遗传算法");}void repchar(FILE *outfp, char *ch, int repcount){int j;for(j=1; j<= repcount; j++)fprintf(outfp, "%s", ch);}void skip(FILE *fp, int skipcount)    //换行数{int j;for(j=1; j<= skipcount; j++)fprintf(outfp,"\n");}void objfunc(struct individual *critter)   /*计算适应度函数值*/{unsigned mask = 1;unsigned bitpos;unsigned tp;double  bitpow;int j,k, stop;critter->varible = 0.0;for(k=0; k< chromsize; k++){if( k== (chromsize-1))stop = lchrom -k*(8*sizeof(unsigned));elsestop = 8*sizeof(unsigned);tp = critter->chrom[k];for(j=0; j< stop; j++){bitpos = j+(8*sizeof(unsigned)) *k;if((tp & mask) == 1){bitpow =(unsigned) pow(2.0, (double)bitpos);critter->varible = critter->varible+bitpow;}tp = tp >> 1;}}    //这里目标函数采用函数f(x)=xsin(10πx)+2critter->varible = -1 + critter->varible*3/(pow(2.0,(double)lchrom) -1);critter->fitness = critter->varible*sin(critter->varible*10*atan(1)*4 )+ 2.0;}void mutation(unsigned *child)   /*变异操作*/{int j,k,stop;unsigned mask, tmp =1 ;for(k=0; k<chromsize; k++){mask = 0;if(k == (chromsize-1))stop = lchrom -(k*(8*sizeof(unsigned)));elsestop = 8*sizeof(unsigned);for(j=0; j< stop; j++){if(flip(pmutation)){mask = mask |(tmp<<j);nmutation ++;}}child[k] = child[k]^mask;}}/*由两个父体交叉产生两个个体*/int crossover(unsigned *parent1, unsigned *parent2, unsigned *child1, unsigned *child2){int j,jcross,k;unsigned mask, temp;if(flip(pcross)){jcross = rnd(1,(lchrom-1));   /*Cross between 1 and -1*/ncross ++;for(k=1; k<=chromsize; k++){if(jcross >= k*(8*sizeof(unsigned))){child1[k-1] = parent1[k-1];child2[k-1] = parent2[k-1];}else if((jcross <(k*(8*sizeof(unsigned)))) && (jcross >((k-1)*(8*sizeof(unsigned))))){mask = 1;for(j=1; j<=(jcross-1-((k-1)*(8*sizeof(unsigned)))); j++){temp = 1;mask = mask << 1;mask = mask | temp;}child1[k-1] = (parent1[k-1] & mask )|(parent2[k-1]&(~mask));child2[k-1] = (parent1[k-1] & (~mask) )|(parent2[k-1]&mask);}else{child1[k-1] = parent2[k-1];child2[k-1] = parent1[k-1];}}}else{for(k=0; k< chromsize; k++){child1[k] = parent1[k];child2[k] = parent2[k];}jcross = 0;}return jcross;}void advance_random()   /*产生55个随机数*/{int j1;double new_random;for(j1 = 0; j1<24; j1++){new_random = oldrand[j1] - oldrand[j1+31];if(new_random< 0.0)new_random = new_random + 1.0;oldrand[j1] = new_random;}for(j1 = 24; j1<55; j1++){new_random = oldrand[j1] - oldrand[j1-24];if(new_random<0.0)new_random = new_random +1.0;oldrand[j1] = new_random;}}int flip(float prob)   /*以一定概率产生0 或 1*/{float radomperc();if(randomperc() <= prob)return (1);elsereturn (0);}void randomize( )   /*设定随机数种子并初始化随机数发生器*/{float randomseed;int j1;for(j1 = 0; j1<=54; j1++)oldrand[j1] = 0.0;jrand = 0;do{printf("随机数种子[0-1]:");scanf("%f", &randomseed);}while((randomseed<0.0) || (randomseed > 1.0));warmup_random(randomseed);}double randomnormaldeviate()      /*产生随机标准差*/{double t,rndx1;if(rndcalcflag){rndx1 = sqrt(-2.0*log((double) randomperc()));t = 6.2831853072*(double)randomperc();rndx2 = rndx1 *sin(t);rndcalcflag = 0;return rndx1*cos(t);}else{rndcalcflag = 1;return rndx2;}}float randomperc()   /*与库函数random()作用相同，产生[0,1]之间一个随机数*/{jrand ++;if(jrand >= 55){jrand = 1;advance_random();}return ((float)oldrand[jrand]);}int rnd(int low,int high)  /*在整数low和high之间产生一个随机数*/{int i;float randomperc();if(low >= high)i = low;else{i = (int)(randomperc()*(high - low +1)) + low;if(i> high)i = high;}return i;}void warmup_random(float random_seed)   /*初始化随机数发生器*/{int j1, ii;double new_random, prev_random;oldrand[54] = random_seed;new_random = 0.000000001;prev_random = random_seed;for(j1 = 1; j1<= 54; j1++){ii = (21*j1)%54;oldrand[ii] = new_random;new_random = prev_random-new_random;if(new_random <0.0) new_random = new_random + 1.0;prev_random = oldrand[ii];}advance_random();advance_random();advance_random();jrand = 0;}int main(){struct individual *temp;/*if(2 > argc){printf("缺少输出文件参数\n");exit(-1);}*//*if((outfp = fopen(argv[1], "w")) == NULL){fprintf(stderr,"Cannot open output file %s\n", argv[1]);exit(-1);}*/printf("输入遗传算法执行次数(1-5):");scanf("%d", &maxruns);for(run =1; run <= maxruns; run ++){initialize();for(gen = 0; gen<maxgen; gen++){fprintf(outfp, "\n 第%d/%d次运行：当前代为%d, 共%d代\n", run, maxruns, gen,maxgen);/*产生新一代*/generation();/*计算新一代种群的适应度统计数据*/statistics(newpop);/*输出新一代统计数据*/report();temp = oldpop;oldpop = newpop;newpop = temp;}freeall();}}