遗传算法java.docx

遗传算法java.docx

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遗传算法java

importjava.io.BufferedReader;

importjava.io.FileInputStream;

importjava.io.FileNotFoundException;

importjava.io.FileOutputStream;

importjava.io.IOException;

importjava.io.InputStreamReader;

importjava.util.ArrayList;

importjava.util.List;

importjava.util.Random;

publicclassGA{

privateChromosome[]chromosomes;

privateChromosome[]nextGeneration;

privateintN;

privateintcityNum;

privatedoublep_c_t;

privatedoublep_m_t;

privateintMAX_GEN;

privateintbestLength;

privateint[]bestTour;

privatedoublebestFitness;

privatedouble[]averageFitness;

privateint[][]distance;

privateStringfilename;

publicGA（）{

N=0;

cityNum=;

p_c_t=0.9;

p_m_t=0.1;

MAX_GEN=00;

bestLength=0;

bestTour=newint[cityNum];

bestFitness=0.0;

averageFitness=newdouble[MAX_GEN];

chromosomes=newChromosome[N];

distance=newint[cityNum][cityNum];

}

/**

*ConstructorofGAclass

*@paramn种群规模

*@paramnum城市规模

*@paramg运行代数

*@paramp_c交叉率

*@paramp_m变异率

*@paramfilename数据文件名

*/

publicGA（intn,intnum,intg,doublep_c,doublep_m,Stringfilename）{

this.N=n;

this.cityNum=num;

this.MAX_GEN=g;

this.p_c_t=p_c;

this.p_m_t=p_m;

bestTour=newint[cityNum];

averageFitness=newdouble[MAX_GEN];

bestFitness=0.0;

chromosomes=newChromosome[N];

nextGeneration=newChromosome[N];

distance=newint[cityNum][cityNum];

this.filename=filename;

}

publicvoidsolve（）throwsIOException{

System.out.println（"---------------------Startinitilization---------------------"）;

init（）;

System.out.println（"---------------------Endinitilization---------------------"）;

System.out.println（"---------------------Startevolution---------------------"）;

for（inti=0;i

System.out.println（"-----------Startgeneration"+i+"----------"）;

evolve（i）;

System.out.println（"-----------Endgeneration"+i+"----------"）;

}

System.out.println（"----------packagetsp;

importjava.io.BufferedReader;

importjava.io.FileInputStream;

importjava.io.IOException;

importjava.io.InputStreamReader;

importjava.util.Random;

publicclassTsp3{

privateintscale;//种群规模

privateintcityNum;//城市数量，染色体长度

privateintMAX_GEN;//运行代数

privatefloat[][]distance;//距离矩阵

privateintbestT;//最佳出现代数

privatefloatbestLength;//最佳长度

privateint[]bestTour;//最佳路径

//初始种群，父代种群，行数表示种群规模，一行代表一个个体，即染色体，列表示染色体基因片段

privateint[][]oldPopulation;

privateint[][]newPopulation;//新的种群，子代种群

privatefloat[]fitness;//种群适应度，表示种群中各个个体的适应度

privatefloat[]Pi;//种群中各个个体的累计概率

privatefloatPc;//交叉概率

privatefloatPm;//变异概率

privateintt;//当前代数

privateRandomrandom;

publicTsp3（）{

}

/**

*constructorofGA

*

*@params

*种群规模

*@paramn

*城市数量

*@paramg

*运行代数

*@paramc

*交叉率

*@paramm

*变异率

*

**/

publicTsp3（ints,intn,intg,floatc,floatm）{

scale=s;

cityNum=n;

MAX_GEN=g;

Pc=c;

Pm=m;

}

//给编译器一条指令，告诉它对被批注的代码元素内部的某些警告保持静默

@SuppressWarnings（"resource"）

/**

*初始化GA算法类

*@paramfilename数据文件名，该文件存储所有城市节点坐标数据

*@throwsIOException

*/

privatevoidinit（Stringfilename）throwsIOException{

//读取数据

//int[]x;

//int[]y;

Stringstrbuff;

BufferedReaderdata=newBufferedReader（newInputStreamReader（

newFileInputStream（filename）））;

distance=newfloat[cityNum][cityNum];

//x=newint[cityNum];

//y=newint[cityNum];

for（inti=0;i

strbuff=data.readLine（）;

String[]strcol=strbuff.split（""）;

for（intj=0;j

distance[i][j]=Float.valueOf（strcol[j]）;

System.out.println（distance[i][j]）;

}

}

data.close（）;

bestLength=Float.MAX_VALUE;

bestTour=newint[cityNum+1];

bestT=0;

t=0;

newPopulation=newint[scale][cityNum];

oldPopulation=newint[scale][cityNum];

fitness=newfloat[scale];

Pi=newfloat[scale];

random=newRandom（System.currentTimeMillis（））;

/*

*for（inti=0;i

*System.out.print（distance[i][j]+","）;}System.out.println（）;}

*/

//初始化种群

}

//初始化种群

voidinitGroup（）{

inti,j,k;

//Randomrandom=newRandom（System.currentTimeMillis（））;

for（k=0;k

{

oldPopulation[k][0]=random.nextInt（65535）%cityNum;

for（i=1;i

{

oldPopulation[k][i]=random.nextInt（65535）%cityNum;

for（j=0;j

if（oldPopulation[k][i]==oldPopulation[k][j]）{

break;

}

}

if（j==i）{

i++;

}