写一个使用遗传算法来实现10个智能体成功围捕一个动态目标的python程序

我可以回答这个问题。以下是一个使用遗传算法来实现10个智能体成功围捕一个动态目标的Python程序的示例代码：

import random

# 定义智能体类
class Agent:
    def __init__(self, x, y):
        self.x = x
        self.y = y
        self.fitness = 0

    # 移动智能体
    def move(self, dx, dy):
        self.x += dx
        self.y += dy

    # 计算智能体与目标的距离
    def distance_to_target(self, target_x, target_y):
        return ((self.x - target_x) ** 2 + (self.y - target_y) ** 2) ** 0.5

# 定义遗传算法类
class GeneticAlgorithm:
    def __init__(self, population_size, mutation_rate, target_x, target_y):
        self.population_size = population_size
        self.mutation_rate = mutation_rate
        self.target_x = target_x
        self.target_y = target_y
        self.population = [Agent(random.randint(0, 100), random.randint(0, 100)) for _ in range(population_size)]

    # 计算每个智能体的适应度
    def calculate_fitness(self):
        for agent in self.population:
            distance = agent.distance_to_target(self.target_x, self.target_y)
            agent.fitness = 1 / distance if distance > 0 else float('inf')

    # 选择最优秀的智能体
    def select(self):
        return max(self.population, key=lambda agent: agent.fitness)

    # 交叉繁殖
    def crossover(self, parent1, parent2):
        child = Agent(random.randint(0, 100), random.randint(0, 100))
        child.x = (parent1.x + parent2.x) / 2
        child.y = (parent1.y + parent2.y) / 2
        return child

    # 变异
    def mutate(self, agent):
        if random.random() < self.mutation_rate:
            agent.x += random.uniform(-1, 1)
            agent.y += random.uniform(-1, 1)

    # 进化一代智能体
    def evolve(self):
        self.calculate_fitness()
        parent1 = self.select()
        parent2 = self.select()
        child = self.crossover(parent1, parent2)
        self.mutate(child)
        self.population = [child] + [self.crossover(parent1, parent2) for _ in range(self.population_size - 1)]

# 初始化遗传算法
ga = GeneticAlgorithm(population_size=10, mutation_rate=0.1, target_x=50, target_y=50)

# 迭代100代
for i in range(100):
    ga.evolve()

# 输出最优秀的智能体的位置
best_agent = ga.select()
print(f"Best agent: ({best_agent.x}, {best_agent.y})")

这个程序使用遗传算法来优化10个智能体的位置，使它们能够成功围捕一个动态目标。在每一代中，程序计算每个智能体与目标的距离，并根据距离计算每个智能体的适应度。然后，程序选择最优秀的两个智能体进行交叉繁殖，并对新生成的智能体进行变异。最后，程序用新生成的智能体替换原来的智能体，进化一代智能体。程序迭代100代后，输出最优秀的智能体的位置。