由于船舶调度问题比较复杂，需要考虑到多个因素，所以以下代码只是简单实现了基于遗传算法的船舶调度问题，仅供参考。
代码如下：
#include
#include
#include
#include
#include
using namespace std;
// 定义船舶和泊位的结构体
struct Ship {
int length; // 船舶长度
int draft; // 船舶吃水深度
int arrivalTime; // 船舶到达时间
int stayTime; // 船舶停留时间
};
struct Berth {
int length; // 泊位长度
int draft; // 泊位吃水深度
int tidalRange; // 泊位潮汐变化
};
// 定义遗传算法的参数
const int POPULATION_SIZE = 50; // 种群数量
const int GENERATION_SIZE = 100; // 迭代次数
const double MUTATION_RATE = 0.05; // 变异率
// 定义全局变量
vector ships; // 船舶列表
vector berths; // 泊位列表
vector<vector > population; // 种群
vector fitness; // 种群适应度
// 随机生成一艘船舶
Ship generateShip() {
Ship ship;
ship.length = rand() % 100 + 100;
ship.draft = rand() % 10 + 10;
ship.arrivalTime = rand() % 1440;
ship.stayTime = rand() % 1440;
return ship;
}
// 随机生成一个泊位
Berth generateBerth() {
Berth berth;
berth.length = rand() % 100 + 200;
berth.draft = rand() % 10 + 10;
berth.tidalRange = rand() % 100;
return berth;
}
// 初始化船舶和泊位列表
void init() {
srand(time(NULL));
for (int i = 0; i < 10; i++) {
ships.push_back(generateShip());
}
for (int i = 0; i < 5; i++) {
berths.push_back(generateBerth());
}
}
// 计算船舶停靠泊位的适应度
int calculateFitness(vector chromosome) {
int fitness = 0;
for (int i = 0; i < chromosome.size(); i++) {
Ship ship = ships[i];
Berth berth = berths[chromosome[i]];
if (ship.length <= berth.length && ship.draft <= berth.draft) {
// 船舶可以停靠该泊位
int arrivalTime = ship.arrivalTime;
int stayTime = ship.stayTime;
int tidalRange = berth.tidalRange;
int leaveTime = arrivalTime + stayTime;
if (leaveTime > 1440) {
leaveTime -= 1440;
}
if (arrivalTime > leaveTime) {
tidalRange = 100 - tidalRange;
}
if (arrivalTime <= 720 && leaveTime <= 720) {
// 泊位潮汐变化不会影响船舶
fitness += 100;
} else if (arrivalTime >= 720 && leaveTime >= 720) {
// 泊位潮汐变化不会影响船舶
fitness += 100;
} else {
// 泊位潮汐变化会影响船舶
fitness += 100 - tidalRange;
}
}
}
return fitness;
}
// 初始化种群
void initPopulation() {
for (int i = 0; i < POPULATION_SIZE; i++) {
vector chromosome;
for (int j = 0; j < ships.size(); j++) {
chromosome.push_back(rand() % berths.size());
}
population.push_back(chromosome);
}
}
// 选择
vector selection() {
int totalFitness = 0;
for (int i = 0; i < fitness.size(); i++) {
totalFitness += fitness[i];
}
vector selectedChromosome;
for (int i = 0; i < POPULATION_SIZE / 2; i++) {
int r1 = rand() % totalFitness;
int r2 = rand() % totalFitness;
int index1 = 0;
int index2 = 0;
int sum = 0;
while (sum <= r1) {
sum += fitness[index1];
index1++;
}
sum = 0;
while (sum <= r2) {
sum += fitness[index2];
index2++;
}
index1--;
index2--;
if (fitness[index1] > fitness[index2]) {
selectedChromosome.insert(selectedChromosome.end(), population[index1].begin(), population[index1].end());
} else {
selectedChromosome.insert(selectedChromosome.end(), population[index2].begin(), population[index2].end());
}
}
return selectedChromosome;
}
// 杂交
void crossover(vector& chromosome1, vector& chromosome2) {
int index = rand() % chromosome1.size();
vector temp(chromosome1.begin() + index, chromosome1.end());
chromosome1.erase(chromosome1.begin() + index, chromosome1.end());
chromosome1.insert(chromosome1.end(), chromosome2.begin() + index, chromosome2.end());
chromosome2.erase(chromosome2.begin() + index, chromosome2.end());
chromosome2.insert(chromosome2.end(), temp.begin(), temp.end());
}
// 变异
void mutation(vector& chromosome) {
for (int i = 0; i < chromosome.size(); i++) {
if ((double)rand() / RAND_MAX < MUTATION_RATE) {
chromosome[i] = rand() % berths.size();
}
}
}
// 迭代
void evolve() {
for (int i = 0; i < GENERATION_SIZE; i++) {
vector<vector > newPopulation;
vector selectedChromosome = selection();
for (int j = 0; j < POPULATION_SIZE / 2; j++) {
vector chromosome1(selectedChromosome.begin() + j * ships.size(), selectedChromosome.begin() + (j + 1) * ships.size());
vector chromosome2(selectedChromosome.begin() + (j + POPULATION_SIZE / 2) * ships.size(), selectedChromosome.begin() + (j + POPULATION_SIZE / 2 + 1) * ships.size());
crossover(chromosome1, chromosome2);
mutation(chromosome1);
mutation(chromosome2);
newPopulation.push_back(chromosome1);
newPopulation.push_back(chromosome2);
}
population = newPopulation;
fitness.clear();
for (int j = 0; j < POPULATION_SIZE; j++) {
fitness.push_back(calculateFitness(population[j]));
}
}
}
// 打印结果
void printResult() {
int maxFitness = 0;
int index = 0;
for (int i = 0; i < fitness.size(); i++) {
if (fitness[i] > maxFitness) {
maxFitness = fitness[i];
index = i;
}
}
cout << "船舶列表：" << endl;
for (int i = 0; i < ships.size(); i++) {
cout << "船舶" << i + 1 << "：长度=" << ships[i].length << " 吃水深度=" << ships[i].draft << " 到达时间=" << ships[i].arrivalTime << " 停留时间=" << ships[i].stayTime << endl;
}
cout << "泊位列表：" << endl;
for (int i = 0; i < berths.size(); i++) {
cout << "泊位" << i + 1 << "：长度=" << berths[i].length << " 吃水深度=" << berths[i].draft << " 潮汐变化=" << berths[i].tidalRange << endl;
}
cout << "最优解：" << endl;
for (int i = 0; i < ships.size(); i++) {
cout << "船舶" << i + 1 << " 停靠泊位" << population[index][i] + 1 << endl;
}
cout << "适应度：" << maxFitness << endl;
}
int main() {
init();
initPopulation();
fitness.clear();
for (int i = 0; i < POPULATION_SIZE; i++) {
fitness.push_back(calculateFitness(population[i]));
}
evolve();
printResult();
return 0;
