prog.cpp

用C++编写基于用遗传算法解一个简单参数优化问题

/***************************************************************/
/* This is a simple genetic algorithm implementation where the */
/* evaluation function takes positive values only and the      */
/* fitness of an individual is the same as the value of the    */
/* objective function                                          */
/***************************************************************/

#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>

/* Change any of these parameters to match your needs */

#define TRUE 1
#define FALSE 0

#define POPSIZE 100               /* population size */
#define MAXGENS 1000             /* max. number of generations */
#define NVARS 2                 /* no. of problem variables */
#define PI 3.14159

#define PXOVER 0.8               /* probability of crossover */
#define PMUTATION 0.001           /* probability of mutation */

int generation;                  /* current generation no. */
int cur_best;                    /* best individual */
FILE *galog;                     /* an output(1) file */
FILE *output;                     /* an output(1) file */

struct genotype /* genotype (GT), a member of the population */
{
  double gene[NVARS];        /* a string of variables */
  double fitness;            /* GT's fitness */
  double upper[NVARS];       /* GT's variables upper bound */
  double lower[NVARS];       /* GT's variables lower bound */
  double rfitness;           /* relative fitness */
  double cfitness;           /* cumulative fitness */
};

struct genotype population[POPSIZE+1];    /* population */
struct genotype newpopulation[POPSIZE+1]; /* new population; */
                                          /* replaces the */
                                          /* old generation */

/* Declaration of procedures used by this genetic algorithm */

void initialize(void);
double randval(double, double);
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);

/***************************************************************/
/* Initialization function: Initializes the values of genes    */
/* within the variables bounds. It also initializes (to zero)  */
/* all fitness values for each member of the population. It    */
/* reads upper and lower bounds of each variable from the      */
/* input file `gadata.txt'. It randomly generates values       */
/* between these bounds for each gene of each genotype in the  */
/* population. The format of the input file `gadata.txt' is    */
/* var1_lower_bound var1_upper bound                           */
/* var2_lower_bound var2_upper bound ...                       */
/***************************************************************/

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);
      }

remove("output.dat");
/* initialize variables within the bounds */

for (i = 0; i < NVARS; i++)
      {
	  // change the format of the data input 2007-9-27	
      fscanf(infile, "%lf\t",&lbound);
      fscanf(infile, "%lf\n",&ubound);
	  	
	  //printf("\nlbound:%lf\t\tubound:%lf",lbound,ubound);	getchar();

      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].gene[i]  = randval(population[j].lower[i],
                                   population[j].upper[i]);
           }
      }

fclose(infile);
}

/***********************************************************/
/* Random value generator: Generates a value within bounds */
/***********************************************************/

double randval(double low, double high)
{
double val;
//srand(time(NULL));
val = ((double)(rand()%1000)/1000.0)*(high - low) + 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[1]^2-x[1]*x[2]+x[3]           */
/*************************************************************/

void evaluate(void)
{
//if ((output = fopen("output.dat","a"))==NULL)
//      {
//      exit(1);
//      }
int mem;
int i;
double x[NVARS+1];

	for (mem = 0; mem < POPSIZE; mem++)
    {
		for (i = 0; i < NVARS; i++)
            x[i+1] = population[mem].gene[i];

		population[mem].fitness = 21.5+x[1]*sin(4*PI*x[1])+x[2]*sin(20*PI*x[2]);//x[1]*sin(10.0*3.14*x[1])+1.0;//x[1]*x[1]+x[2]*x[2];
	  
		if (population[mem].fitness <40 && population[mem].fitness>35)
		{
			  fprintf(output, "\n%5d,      %6.9f, %6.9f, %6.9f ", generation, 
											x[1],x[2],population[mem].fitness);
		}

    }
}

/***************************************************************/
/* Keep_the_best function: This function keeps track of the    */
/* best member of the population. Note that the last entry in  */
/* the array Population holds a copy of the best individual    */
/***************************************************************/

void keep_the_best()
{
int mem;
int i;
cur_best = 0; /* stores the index of the best individual */

for (mem = 0; mem < POPSIZE; mem++)
      {
      if (population[mem].fitness > population[POPSIZE].fitness)
            {
            cur_best = mem;
            population[POPSIZE].fitness = population[mem].fitness;
            }
      }
/* once the best member in the population is found, copy the genes */
for (i = 0; i < NVARS; i++)
      population[POPSIZE].gene[i] = population[cur_best].gene[i];
}

/****************************************************************/
/* Elitist function: The best member of the previous generation */
/* is stored as the last in the array. If the best member of    */
/* the current generation is worse then the best member of the  */
/* previous generation, the latter one would replace the worst  */
/* member of the current population                             */
/****************************************************************/

void elitist()
{
int i;
double best, worst;             /* best and worst fitness values */
int best_mem, worst_mem; /* indexes of the best and worst member */

best = population[0].fitness;
worst = population[0].fitness;
for (i = 0; i < POPSIZE - 1; ++i)
      {
      if(population[i].fitness > population[i+1].fitness)
            {      
            if (population[i].fitness >= best)
                  {
                  best = population[i].fitness;
                  best_mem = i;
                  }
            if (population[i+1].fitness <= worst)
                  {
                  worst = population[i+1].fitness;
                  worst_mem = i + 1;
                  }
            }
      else
            {
            if (population[i].fitness <= worst)
                  {
                  worst = population[i].fitness;
                  worst_mem = i;
                  }
            if (population[i+1].fitness >= best)
                  {
                  best = population[i+1].fitness;
                  best_mem = i + 1;
                  }
            }
      }
/* if best individual from the new population is better than */
/* the best individual from the previous population, then    */
/* copy the best from the new population; else replace the   */
/* worst individual from the current population with the     */
/* best one from the previous generation                     */

if (best >= population[POPSIZE].fitness)
    {
    for (i = 0; i < NVARS; i++)
       population[POPSIZE].gene[i] = population[best_mem].gene[i];
    population[POPSIZE].fitness = population[best_mem].fitness;
    }
else
    {
    for (i = 0; i < NVARS; i++)
       population[worst_mem].gene[i] = population[POPSIZE].gene[i];
    population[worst_mem].fitness = population[POPSIZE].fitness;
    } 
}
/**************************************************************/
/* Selection function: Standard proportional selection for    */
/* maximization problems incorporating elitist model - makes  */
/* sure that the best member survives                         */
/**************************************************************/

// sort the fitness of population from small to larger
void bubble_sort(int *(pos[]))
{
	int i=0,j=0,flag=0,k=0,temp=0;
	j=POPSIZE-2;
	flag=0;   //flag=0表示排序完成，flag=1表示记录有序，无须进行比较
	k=POPSIZE-1;
	while(flag==0&&j>=0)
	{
		flag=1;
		for(i=0;i<=j;i++)
		{
			if(population[ (*pos)[i] ].fitness > population[ (*pos)[i+1] ].fitness )
			{
				temp=(*pos)[i];
				(*pos)[i]=(*pos)[i+1];
				(*pos)[i+1]=temp;
				flag=0;
				k=i;
			}
			j=k-1;
		}
	}
}

// 排序选择
void select_ranking(void)
{
	int i=0,j=0;
	double p=0;

	int* pos=(int*)malloc(POPSIZE*sizeof(int)); //记录适应度大小排序后的位置，由小至大
	for(i=0;i<POPSIZE;i++)
		pos[i]=i;
	
	bubble_sort(&pos);

	for (i = 0; i < POPSIZE; i++)
	{
		  population[i].rfitness =  2.0*(double)(pos[i]+1)/(POPSIZE*(POPSIZE+1));
	}
	population[0].cfitness = population[0].rfitness;

	/* calculate cumulative fitness */
	for (i = 1; i < POPSIZE; i++)
	{
		  population[i].cfitness =  population[i-1].cfitness +       
							  population[i].rfitness;
	}

	/* finally select survivors using cumulative fitness. */
	for (i = 0; i < POPSIZE; i++)
	{ 
		  p = rand()%1000/1000.0;
		  if (p < population[0].cfitness)
				newpopulation[i] = population[0];      
		  else
				{
				for (j = 0; j < POPSIZE;j++)      
					  if (p >= population[j].cfitness && 
								  p<population[j+1].cfitness)
							newpopulation[i] = population[j+1];
				}
		  }
	/* once a new population is created, copy it back */

	for (i = 0; i < POPSIZE; i++)
		  population[i] = newpopulation[i];  

}

// 轮盘赌
void select(void)
{
int mem, i, j;
double sum = 0;
double p;