遗传算法并行源程序.docx

1、遗传算法并行源程序/THBGFSHFGBCVXNCH#define WIN32_LEAN_AND_MEAN / 从 Windows 头中排除极少使用的资料#include #include #include #include #include #include #include #include #pragma comment(lib,Wininet.lib) #pragma comment(lib,mpi.lib)#pragma comment(lib,cxx.lib)#define POPSIZE 6 /* 群体大小，其实就是基因总数*/#define MAXGENS 10 /* 进化代数

2、 */#define NVARS 3 /* 问题变量数，即基因的长度*/#define PXOVER 0.8 /* 交叉概率 */#define PMUTATION 0.15 /* 变异概率 */#define TRUE 1#define FALSE 0int generation; /* 记录当前已经进化的代数 */int cur_best; /* 最优个体 */FILE *galog; /* 日志文件*/struct genotype /* 基因类型*/double geneNVARS; /* 染色体字符串*/double upperNVARS; /* 每个基于的上界*/double lo

3、werNVARS; /* 每个基因的下界*/double fitness; /* 个体适应度*/double rfitness; /* 相对适应度*/double cfitness; /* 累积适应度*/;struct genotype populationPOPSIZE+1; /* 种群*/struct genotype newpopulationPOPSIZE+1; /* 新种群*/void initialize(void); /* 种群初始化函数*/double randval(double, double); /* 随机函数*/void keep_the_best(void); /*寻

4、找最优个体*/ void elitist(void); /* 保持最优*/void select(void); /* 选择算子*/void crossover(void); /* 交叉算子*/void Xover(int,int); /* 交叉操作*/void swap(double *, double *); /* 交换*/void evaluate(void); /* 个体适应度评价*/void worker(genotype *,int ) /* 从处理器操作,突变和适应度评价*/void report(void); /* 记录*/*初始化基因值，适应值。从gadata.txt中读入每个

5、变量的上下限，然后随机产生。*/void initialize(void)FILE *infile;int i, j;double lbound, ubound;if (infile = fopen(gadata.txt,r)=NULL) fprintf(galog,nCannot open input file!n); exit(1);for (i = 0; i NVARS; i+)/nvars=3,问题变量数 fscanf(infile, %lf,&lbound); fscanf(infile, %lf,&ubound); for (j = 0; j POPSIZE; j+)/50 pop

6、ulationj.fitness = 0; populationj.rfitness = 0; populationj.cfitness = 0; populationj.loweri = lbound; populationj.upperi= ubound; populationj.genei = randval(populationj.loweri, populationj.upperi); fclose(infile);/*/* 在上下界间产生一个数 */*/double randval(double low, double high)double val;val = (double)(

7、rand()%1000)/1000.0)*(high - low) + low;return(val);/*/* Keep_the_best function: 保持对最优个体的追踪 */*/void keep_the_best()/将最优的值放在最后一个空位上int mem;int i;cur_best = 0; /* 最优个体索引 */for (mem = 0; mem populationPOPSIZE.fitness) cur_best = mem; populationPOPSIZE.fitness = populationmem.fitness; /* once the best

8、member in the population is found, copy the genes */for (i = 0; i NVARS; i+) populationPOPSIZE.genei = populationcur_best.genei;/*/* Elitist function: 如果这一代的最好值比上一代的 */* 差，那么用上一代的最好值替代本代的最差值 */*/void elitist()int i;double best, worst; /* best and worst fitness values */int best_mem, worst_mem; /* in

9、dexes of the best and worst member */best = population0.fitness;worst = population0.fitness;for (i = 0; i populationi+1.fitness) if (populationi.fitness = best) best = populationi.fitness; best_mem = i; if (populationi+1.fitness = worst) worst = populationi+1.fitness; worst_mem = i + 1; else if (pop

10、ulationi.fitness = best) best = populationi+1.fitness; best_mem = i + 1; /* 如果新一代种群中的最优个体比前一代的最优个体好， */* best取此值，反之 用上一代最优个体取代当前代的最差个体。 */if (best = populationPOPSIZE.fitness) for (i = 0; i NVARS; i+) populationPOPSIZE.genei = populationbest_mem.genei; populationPOPSIZE.fitness = populationbest_mem.

11、fitness;else for (i = 0; i NVARS; i+) populationworst_mem.genei = populationPOPSIZE.genei; populationworst_mem.fitness = populationPOPSIZE.fitness; /*/* Selection function: 适者生存通过适应度大小筛选，适 */* 应度越大，出现的次数越多（会有重复） */*/void select(void)/int mem, i, j, k=0;double sum = 0;double p;/* 计算适应度总和*/for (mem =

12、0; mem POPSIZE; mem+) sum += populationmem.fitness;/* 计算相对适应度，有点像概率密度*/for (mem = 0; mem POPSIZE; mem+) populationmem.rfitness = populationmem.fitness/sum;/* 计算累计适应度，有点像分布函数*/population0.cfitness = population0.rfitness;for (mem = 1; mem POPSIZE; mem+) populationmem.cfitness = populationmem-1.cfitnes

13、s + populationmem.rfitness;/* 使用累计适应度确定幸存者,落在哪个概率区域就选择相应的概率区域上的染色体*/for (i = 0; i POPSIZE; i+) p = rand()%1000/1000.0; if (p population0.cfitness) newpopulationi = population0;/选择 else for (j = 0; j = populationj.cfitness & ppopulationj+1.cfitness) newpopulationi = populationj+1;/选择 /* 确定全部的幸存者后，拷贝回

14、population完成选择*/for (i = 0; i POPSIZE; i+) populationi = newpopulationi;/*/* Crossover selection: 单随机点交叉，前后两个 */* 个体交叉，得到两个新的个体，原来的个体消失 */*/void crossover(void)int i=0, mem, one;int first = 0; /* count of the number of members chosen */double x;for (mem = 0; mem POPSIZE; +mem) x = rand()%1000/1000.0

15、; if (x 1) /等于1时就没有交叉的必要 if(NVARS = 2) point = 1; else point = (rand() % (NVARS - 1) + 1; for (i = 0; i point; i+) swap(&populationone.genei, &populationtwo.genei);/*/* Swap: 互换 */*/void swap(double *x, double *y)double temp;temp = *x;*x = *y;*y = temp;void evaluate(void) /*个体适应度评价函数*/int mem;int i;

16、double xNVARS+1;for (mem = 0; mem POPSIZE; mem+) for (i = 0; i NVARS; i+) xi+1 = populationmem.genei; populationmem.fitness = x1*x1 + x2*x2 + x3*x3;/计算得到适应度void report(void) double best_val; /* 种群中最高适应度值*/ double avg; /* 平均适应度值*/ double stddev; /* 标准方差*/ double sum_square; /* 平方的总数*/ double square_s

17、um; /* 总数的平方*/ double sum; /* 适应度值总和*/ sum = 0.0; sum_square = 0.0; for (int i = 0; i POPSIZE; i+) sum += populationi.fitness; sum_square += populationi.fitness * populationi.fitness; avg = sum/(double)POPSIZE; square_sum = avg * avg * POPSIZE; stddev = sqrt(sum_square - square_sum)/(POPSIZE - 1); b

18、est_val = populationPOPSIZE.fitness; fprintf(galog, %5d tt %6.3f t %6.3f t %6.3fn, generation, best_val, avg, stddev); printf(%5d tt %6.3f t %6.3f t %6.3fn, generation, best_val, avg, stddev);void worker(genotype * subpopulation,int mysize) /*突变和适应度评价操作*/double lbound, hbound;double x;for (int i = 0

19、; i mysize; i+) for (int j = 0; j NVARS; j+) x = rand()%1000/1000.0; if (x lowerj; hbound = subpopulation-upperj; subpopulationi.genej = randval(lbound, hbound); /end 变异/适应度评价int mem;double xchormNVARS+1;for (mem = 0; mem mysize; mem+) for (int i = 0; i 1) mysize = POPSIZE/(numprocs-1);/每个从处理器上应该分配的

20、个体数else if (numprocs=1) mysize= POPSIZE;genotype * subpopulation = (genotype*)malloc(mysize*sizeof(genotype);if (!subpopulation) printf(不能获得 %d 个大小的空间n,mysize); MPI_Abort(MPI_COMM_WORLD,1);/自定义数据类型MPI_Datatype mystruct;int blocklens6;MPI_Aint indices6;MPI_Datatype old_types6;/各块中数据个数blocklens0 = NVA

21、RS;blocklens1 = NVARS;blocklens2 = NVARS;blocklens3 = 1;blocklens4 = 1;blocklens5 = 1;/数据类型for (i =0;igene,&indices0);MPI_Address(&population-upper,&indices1);MPI_Address(&population-lower,&indices2);MPI_Address(&population-fitness,&indices3);MPI_Address(&population-rfitness,&indices4);MPI_Address(&population-cfitness,&indices5);indices5=indices5-indices0;indices4=indices4-indices0;indices3=indices3-indices0;indices2=indices2-indices0;indices1=indices1-indices0;indices0=0;MPI_Type_struct(6,blocklens,indices,old_types,&mystruct);/生成新的mpi数据类型MPI_Type_commit(&mystruct);/输出表头，初始化，初步测评，初步寻优if(my