基于遗传算法路径规划的C++代码内容运行结果得有图片

很抱歉，我无法提供图片。但是我可以给您提供一个基于遗传算法的路径规划的C代码示例：

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

#define POP_SIZE 100  // 种群大小
#define MAX_GEN 500   // 最大迭代次数
#define PXOVER 0.8    // 交叉概率
#define PMUTATION 0.15 // 变异概率
#define CITY_NUM 50    // 城市数量
#define DN 1000000.0   // 距离归一化因子

struct city{
    float x;
    float y;
};

struct chromosome{
    int gene[CITY_NUM];
    float distance;
};

struct city cities[CITY_NUM];
struct chromosome population[POP_SIZE];
struct chromosome best_chromosome;

// 计算两个城市之间的距离
float distance(struct city a, struct city b){
    return (float)sqrt((a.x - b.x) * (a.x - b.x) + (a.y - b.y) * (a.y - b.y))) / DN;
}

// 初始化种群
void init_population(void){
    int i, j, k;
    for (i = 0; i < POP_SIZE; i++){
        for (j = 0; j < CITY_NUM; j++){
            population[i].gene[j] = j;
        }
        for (j = 0; j < CITY_NUM; j++){
            k = rand() % CITY_NUM;
            int temp = population[i].gene[j];
            population[i].gene[j] = population[i].gene[k];
            population[i].gene[k] = temp;
        }
    }
}

// 计算染色体的总路程
void calc_distance(struct chromosome *chrom){
    int i;
    chrom->distance = 0.0;
    for (i = 0; i < CITY_NUM - 1; i++){
        chrom->distance += distance(cities[chrom->gene[i]], cities[chrom->gene[i + 1]]);
    }
    chrom->distance += distance(cities[chrom->gene[CITY_NUM - 1]], cities[chrom->gene[0]]);
}

// 评估种群中每个染色体的适应度
void evaluate_population(void){
    int i;
    for (i = 0; i < POP_SIZE; i++){
        calc_distance(&population[i]);
        if (population[i].distance < best_chromosome.distance){
            best_chromosome = population[i];
        }
    }
}

// 选择操作
void selection(struct chromosome *parent1, struct chromosome *parent2){
    int i, j;
    i = rand() % POP_SIZE;
    j = rand() % POP_SIZE;
    if (population[i].distance < population[j].distance){
        *parent1 = population[i];
    }
    else{
        *parent1 = population[j];
    }
    i = rand() % POP_SIZE;
    j = rand() % POP_SIZE;
    if (population[i].distance < population[j].distance){
        *parent2 = population[i];
    }
    else{
        *parent2 = population[j];
    }
}

// 交叉操作
void crossover(struct chromosome parent1, struct chromosome parent2, struct chromosome *child1, struct chromosome *child2){
    int i, j, k;
    int index1, index2;
    int temp[CITY_NUM];
    index1 = rand() % CITY_NUM;
    index2 = rand() % CITY_NUM;
    if (index1 > index2){
        k = index1;
        index1 = index2;
        index2 = k;
    }
    for (i = index1; i <= index2; i++){
        child1->gene[i] = parent1.gene[i];
        child2->gene[i] = parent2.gene[i];
    }
    j = 0;
    k = 0;
    for (i = 0; i < CITY_NUM; i++){
        if (j == index1){
            j = index2 + 1;
        }
        if (k == index1){
            k = index2 + 1;
        }
        for (; j <= index2; j++){
            if (parent2.gene[i] == parent1.gene[j]){
                break;
            }
        }
        for (; k <= index2; k++){
            if (parent1.gene[i] == parent2.gene[k]){
                break;
            }
        }
        if (j == index2 + 1){
            child1->gene[k] = parent2.gene[i];
        }
        if (k == index2 + 1){
            child2->gene[j] = parent1.gene[i];
        }
    }
}

// 变异操作
void mutation(struct chromosome *chrom){
    int i, j, k;
    i = rand() % CITY_NUM;
    j = rand() % CITY_NUM;
    k = chrom->gene[i];
    chrom->gene[i] = chrom->gene[j];
    chrom->gene[j] = k;
}

// 遗传算法主函数
void ga_main(void){
    int i, j;
    struct chromosome parent1, parent2;
    struct chromosome child1, child2;
    for (i = 0; i < MAX_GEN; i++){
        for (j = 0; j < POP_SIZE / 2; j++){
            selection(&parent1, &parent2);
            if (rand() < PXOVER * RAND_MAX){
                crossover(parent1, parent2, &child1, &child2);
            }
            else{
                child1 = parent1;
                child2 = parent2;
            }
            if (rand() < PMUTATION * RAND_MAX){
                mutation(&child1);
            }
            if (rand() < PMUTATION * RAND_MAX){
                mutation(&child2);
            }
            population[j * 2] = child1;
            population[j * 2 + 1] = child2;
        }
        evaluate_population();
    }
}

// 主函数
int main(int argc, char *argv[]){
    int i;
    srand((unsigned)time(NULL));
    for (i = 0; i < CITY_NUM; i++){
        cities[i].x = (float)rand() / RAND_MAX;
        cities[i].y = (float)rand() / RAND_MAX;
    }
    init_population();
    evaluate_population();
    ga_main();
    printf("Best distance: %f\n", best_chromosome.distance);
    printf("Best path: ");
    for (i = 0; i < CITY_NUM; i++){
        printf("%d ", best_chromosome.gene[i]);
    }
    printf("\n");
    return 0;
}

这是一个简单的遗传算法路径规划的C代码，其中包括了初始化种群、计算染色体总路程、评估适应度、选择、交叉、变异等操作。运行结果会输出最优路径的距离和路径序列。