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/***************************************************************/
/* 程序功能： 實現小生境遺傳算法 */
/* 參考文獻： 《遺傳算法原理及應用》 */
/* 作者 ： 周明 孫樹棟 */
/* 出版社 ： 國防工業出版社(2001年第二版) */
/* 程序實現： 蔣龍聰 */
/* 單位 ： 中國地質大學(武漢)地球物理與空間信息學院 */
/* 專業 ： 地球探測與信息技術 */
/* 研究方向： 地球物理數據處理及其地震層析成像 */
/* 説明 ： 採用了Denis Cormier和Sita S.Raghavan的程序框架圖*/
/***************************************************************/
#include <stdio.h >
#include <stdlib.h>
#include <math.h>
#include <time.h>
/* 初始化遺傳算法參數，讀者可以根據實際問題修改設置參數值的大小*/
#define POPSIZE 100 /* 種群大小 */
#define N 30 /* 小生境保留值 */
#define MAXGENS 200 /* 最大進化代數 */
#define NVARS 4 /* 反演參數的個數 */
#define PXOVER 0.8 /* 交叉概率 */
#define PMUTATION 0.2 /* 變異概率 采用實數編碼 變異率可以放大些 配合非均勻變異 */
#define TRUE 1
#define FALSE 0
#define Lenth 120 /* 產生Gauss正態分佈而用的均勻分佈的長度 */
#define PI 3.1415926 /* 圓周率 */
#define RECEIVES 1001 /* 地震波形記錄點數 */
#define shapeFactor 6 //非均勻變異中的形狀因子 //越大收斂早！~
#define HD 0.1 /*海明距離*/
int generation; /* 當前代數值 */
int cur_best; /* 最佳個體 */
double Penalty;
double Gfactor;
double OBS[RECEIVES];
double h[3]={50,20,50};
FILE *galog; /* 輸出文件 */
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 cfitness; /* cumulative fitness */
};
struct genotype population[POPSIZE+1]; /* population */
struct genotype newpopulation[POPSIZE+1]; /* new population; */
struct genotype nicheP[POPSIZE+N]; /* replaces the */
/* old generation */
struct genotype popMemory[N];
/* Declaration of procedures used by this genetic algorithm */
void initialize(void);
void evaluate(void);
void searchBest(void);
void elitist(void);
void select(void);
void crossover(void);
void Xover(int,int);
void swap(double*,double*);
void mutate(void);
void nicheGA(void);
void simulate(void);
double randval(double, double);
double randvalG(double,double);
double randvalNu(int,int,double,double);
double obFun(double x[]);
double hamming(int,int);
double hamming1(int,int);
/***************************************************************/
/* 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;
double sum=0.0;
if ((infile = fopen("gadata.txt","r"))==NULL)
{
fprintf(galog,"\nCannot open input file!\n");
exit(1);
}
/* 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].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);
//產生第一個初始群體，爲了保持群體的差異性，引入Hamming距離加以控制和約束
/*
j=0;
for(i=0;i<NVARS; i++)
population[j].gene[i]=randval(population[j].lower[i],population[j].upper[i]);
while(j<POPSIZE)
{
j++;
for(i=0; i<NVARS; i++)
{
population[j].gene[i]=randval(population[j].lower[i],population[j].upper[i]);
}
if(hamming(j,j-1)<HD)
j--;
}
*/
}
/***********************************************************/
/* Calculate the Hamming distance of two dataset */
/***********************************************************/
double hamming(int i,int j)
{
int k=0;
double sum=0;
for(k=0;k<NVARS;k++)
sum=sum+(population[i].gene[k]-population[j].gene[k])*(population[i].gene[k]-population[j].gene[k]);
return(sqrt(sum));
}
double hamming1(int i,int j)
{
int k=0;
double sum=0;
for(k=0;k<NVARS;k++)
sum=sum+(nicheP[i].gene[k]-nicheP[j].gene[k])*(nicheP[i].gene[k]-nicheP[j].gene[k]);
return(sqrt(sum));
}
/***********************************************************/
/* Random value generator: Generates a value within bounds */
/* Generate uniform random numbers,the mutation is uniform */
/***********************************************************/
double randval(double low, double high)
{
double val;
val = low+((double)(rand()%1000)/1000.0)*(high-low);
return(val);
}
/***********************************************************/
/* Random value generator: Generates a value within bounds */
/* Generate Gauss distribution random numbers,mutation also*/
/*****???????????????????????????????????????????????*******/
double randvalG(double low,double high)
{
int i=0;
double val,sum=0.0;
for(i=0;i<Lenth;i++)
{
sum+=(double)(rand()%500)/500.0;
}
return (val=(low+high)/2+(high-low)*(sum-Lenth/2)/(Lenth/2));
}
/***********************************************************/
/* Random value generator: Generates a value within bounds */
/* Generate Non-unform random numbers,also the mutation */
/*****???????????????????????????????????????????***********/
double randvalNu(int i,int j,double low,double high)
{
double val;
double r=generation/MAXGENS;
double randN=(rand()%500)/500.0;
if( (int) (randN+0.5)==0 )
val=population[i].gene[j]+(high-population[i].gene[j])*(pow(randN*(1-r),shapeFactor));
if( (int) ( randN+0.5)==1 )
val=population[i].gene[j]-(population[i].gene[j]-low)*(pow(randN*(1-r),shapeFactor));
return(val); //非均勻變異
}
/*************************************************************/
/* Calculate the object function */
/* I chose Shuber function as a test function,while X[-10 10]*/
/* This function has the global minimum value,F(X)=-186.731 */
/*************************************************************/
double obFun(double x[])
{
double g[RECEIVES],b[201],r[RECEIVES];//定義數組
double dt=0.5,f0=60; //數組賦值
double t[3];//時間
double v[4];
double SUM=0.0;
int mx=1;
int i=0,j=0,nw=100;
v[0]=x[0];
v[1]=x[1];
v[2]=x[2];
v[3]=x[3];
//初始化反射系數數組
for(i=0;i<RECEIVES;i++)
r[i]=0.0;
t[0]=2*h[0]/v[0];
j=(int)(1000*t[0]/dt);
r[j]=(v[1]-v[0])/(v[1]+v[0]); //1層
for(i=1;i<NVARS;i++)
{
t[i]=t[i-1]+2*h[i]/v[i];
j=(int)(1000*t[i]/dt);
r[j]=(v[i+1]-v[i])/(v[i+1]+v[i]);//2-4層
}
for(i=-nw;i<nw+1;i++)
{
double a=(0.001*PI*f0*i*dt);
b[i+nw]=(1.0-2.0*a*a)*exp(-a*a);//求取子波，0相位，
}
//Convolution 子波和參數做卷積
for(i=0;i<RECEIVES;i++)//從第一道循環
{
double sum=0.0;
for(j=0;j<2*nw+1;j++)
{
if(i-j>=0&&i-j<=2*nw)
sum=sum+b[j]*r[i-j+1];
}
g[i]=sum;
}
for(i=0;i<RECEIVES;i++) //目標函數
SUM+=(OBS[i]-g[i])*(OBS[i]-g[i]);
SUM=sqrt(SUM);
return(5000.0-SUM);
}
/*************************************************************/
/* Evaluation function: This takes a user defined function. */
/*************************************************************/
void evaluate(void)
{
int mem;
int i;
double x[NVARS];
for (mem = 0; mem < POPSIZE; mem++)
{
for (i=0;i<NVARS;i++)
x[i] = population[mem].gene[i];
population[mem].fitness =obFun(x);
}
}
/***************************************************************/
/* searchBest 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 */
/***************從大到小排序排序********************************/
void searchBest()
{
int mem;
int i;
struct genotype tempGA;
for (mem=0;mem<POPSIZE-1;mem++)
{
for(i=mem+1;i<POPSIZE;i++)
if (population[mem].fitness<population[i].fitness)
{
tempGA=population[mem];
population[mem]=population[i];
population[i]=tempGA;
}
}
for(mem=0;mem<N;mem++)
popMemory[mem]=population[mem];//記憶前N個基因
// once the best member in the population is found, copy the genes
population[POPSIZE]=population[0];
// for (i = 0; i < NVARS; i++)
// population[POPSIZE].gene[i] = population[0].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 than the best member of the */
/* previous generation, the latter one would replace the worst */
/* member of the current population(保留最佳策略方案) */
/****************************************************************/
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;
}
}
}
printf("BestFitness=%8.4f \n",-best+5000.0);
/* 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;
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