unit1.c

一个生产工序安排的算法（采用遗传算法

//---------------------------------------------------------------------------

#pragma hdrstop

//---------------------------------------------------------------------------

#pragma argsused
//int main(int argc, char* argv[])
//{
//        return 0;
//}
//---------------------------------------------------------------------------



/**************************************************************************/
/* 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>

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

#define POPSIZE 50               /* population size */
#define MAXGENS 60             /* max. number of generations */
#define NVARS 8                  /* no. of problem variables */
#define PXOVER 0.89               /* probability of crossover */
#define PMUTATION 0.29           /* probability of mutation */
#define TRUE 1
#define FALSE 0

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

#define NMACHINE 4          /* Number of machines */
#define MAXTIME 1000000          /* THE MAX OF ALL CONSUMING TIME.. (UNIT IN SECOND) */

double time_consuming[NMACHINE][NVARS];

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);
unsigned int randval(void);
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);
      }

/* initialize the time_consuming[][] from file */
for(i = 0; i < NVARS; i++) 
	{
	for(j = 0; j < NMACHINE; j ++) 
		{
		fscanf(infile, "%lf",&time_consuming[j][i]);
		}
	}


/* initialize variables within the bounds */

for (i = 0; i < NVARS; i++)
      {
      fscanf(infile, "%lf",&lbound);
      fscanf(infile, "%lf",&ubound);

      for (j = 0; j < POPSIZE; j++)
           {
           population[j].fitness = 0;
           population[j].rfitness = 0;
           population[j].cfitness = 0;
           population[j].lower[i] = 0;
           population[j].upper[i]= 0;
           population[j].gene[i] = randval();
           }
      }

fclose(infile);
}

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

unsigned int randval(void)
{
double val;
val = ((double)(rand()%1000)/1000.0);
return ((unsigned int)(val*NMACHINE));
}

/*************************************************************/
/* 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]           */
/*************************************************************/
/* ****************************************************** */
/* Here is the user defined function:  the time consuming is less */

void evaluate(void)
{
int mem;
int i;
int machine;
double consuming[NMACHINE];
double max_consuming;

for (mem = 0; mem < POPSIZE; mem++)
      {
      for(i = 0; i < NMACHINE; i ++) consuming[i] = 0; // clear
      for (i = 0; i < NVARS; i++) {
      	machine = population[mem].gene[i];
  			if(machine >= NMACHINE) { // not neede for speed here, but filter the data 
					fprintf(galog,"\nThe initialize error!\n");
					exit(1);
				}
        consuming[machine] += time_consuming[machine][i];
      }
      // get the maxium time as fitness.
      max_consuming = 0;;
      for(i = 0; i < NMACHINE; i ++) {
      	if(consuming[i] > max_consuming) 
      		max_consuming = consuming[i];
      }
      population[mem].fitness = MAXTIME - max_consuming;
      }      
}

/***************************************************************/
/* 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;