遗传算法的并行实现程序
时间: 2023-11-10 13:06:23 浏览: 31
以下是一个简单的遗传算法并行实现的示例程序,使用OpenMP库来实现并行计算:
```
#include <iostream>
#include <omp.h>
using namespace std;
// 遗传算法参数
const int POP_SIZE = 100; // 种群大小
const int CHROMO_SIZE = 10; // 染色体长度
const int MAX_GENERATIONS = 100; // 最大迭代次数
const double MUTATION_RATE = 0.01; // 变异率
const double CROSSOVER_RATE = 0.8; // 交叉率
// 遗传算法函数
void genetic_algorithm(int **population) {
// 初始化种群
#pragma omp parallel for
for (int i = 0; i < POP_SIZE; i++) {
for (int j = 0; j < CHROMO_SIZE; j++) {
population[i][j] = rand() % 2;
}
}
// 迭代遗传算法
for (int generation = 0; generation < MAX_GENERATIONS; generation++) {
// 计算适应度
double *fitness = new double[POP_SIZE];
#pragma omp parallel for
for (int i = 0; i < POP_SIZE; i++) {
int sum = 0;
for (int j = 0; j < CHROMO_SIZE; j++) {
sum += population[i][j];
}
fitness[i] = sum / (double) CHROMO_SIZE;
}
// 选择
int **new_population = new int*[POP_SIZE];
#pragma omp parallel for
for (int i = 0; i < POP_SIZE; i++) {
int parent1 = rand() % POP_SIZE;
int parent2 = rand() % POP_SIZE;
if (fitness[parent1] > fitness[parent2]) {
new_population[i] = population[parent1];
} else {
new_population[i] = population[parent2];
}
}
// 交叉
#pragma omp parallel for
for (int i = 0; i < POP_SIZE; i += 2) {
if (rand() < CROSSOVER_RATE * RAND_MAX) {
int crossover_point = rand() % CHROMO_SIZE;
for (int j = crossover_point; j < CHROMO_SIZE; j++) {
int temp = new_population[i][j];
new_population[i][j] = new_population[i+1][j];
new_population[i+1][j] = temp;
}
}
}
// 变异
#pragma omp parallel for
for (int i = 0; i < POP_SIZE; i++) {
for (int j = 0; j < CHROMO_SIZE; j++) {
if (rand() < MUTATION_RATE * RAND_MAX) {
new_population[i][j] = 1 - new_population[i][j];
}
}
}
// 更新种群
#pragma omp parallel for
for (int i = 0; i < POP_SIZE; i++) {
for (int j = 0; j < CHROMO_SIZE; j++) {
population[i][j] = new_population[i][j];
}
}
// 释放内存
delete[] new_population;
delete[] fitness;
}
}
int main() {
// 初始化种群矩阵
int **population = new int*[POP_SIZE];
for (int i = 0; i < POP_SIZE; i++) {
population[i] = new int[CHROMO_SIZE];
}
// 运行遗传算法
double start_time = omp_get_wtime();
genetic_algorithm(population);
double end_time = omp_get_wtime();
// 输出结果
cout << "Time taken: " << end_time - start_time << " seconds" << endl;
// 释放内存
for (int i = 0; i < POP_SIZE; i++) {
delete[] population[i];
}
delete[] population;
return 0;
}
```
以上是一个简单的遗传算法并行实现的示例程序,使用OpenMP库来实现并行计算。其中,使用#pragma omp parallel for来实现并行计算,将循环语句并行化。具体实现需要根据实际情况选择合适的并行实现方式。
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