遗传模拟退火算法代码

遗传模拟退火算法是一种优化算法，用于在搜索空间中找到最优解。下面是一个简单的遗传模拟退火算法的代码示例：

import random
import math

# 定义问题的目标函数
def objective_function(x):
    return x**2

# 定义遗传模拟退火算法
def genetic_simulated_annealing():
    # 初始化参数
    population_size = 100  # 种群大小
    chromosome_length = 10  # 染色体长度
    mutation_rate = 0.01  # 变异率
    temperature = 100  # 初始温度
    cooling_rate = 0.95  # 冷却率

    # 初始化种群
    population = []
    for _ in range(population_size):
        chromosome = [random.randint(0, 1) for _ in range(chromosome_length)]
        population.append(chromosome)

    # 迭代搜索
    while temperature > 0.01:
        for i in range(population_size):
            # 计算个体适应度
            x = int(''.join(map(str, population[i])), 2)
            fitness = objective_function(x)

            # 生成新个体
            new_chromosome = population[i].copy()
            for j in range(chromosome_length):
                if random.random() < mutation_rate:
                    new_chromosome[j] = 1 - new_chromosome[j]

            # 计算新个体适应度
            new_x = int(''.join(map(str, new_chromosome)), 2)
            new_fitness = objective_function(new_x)

            # 判断是否接受新个体
            if new_fitness < fitness or random.random() < math.exp((fitness - new_fitness) / temperature):
                population[i] = new_chromosome

        # 降低温度
        temperature *= cooling_rate

    # 返回最优解
    best_chromosome = population[0]
    for chromosome in population:
        x = int(''.join(map(str, chromosome)), 2)
        if objective_function(x) < objective_function(int(''.join(map(str, best_chromosome)), 2)):
            best_chromosome = chromosome

    return int(''.join(map(str, best_chromosome)), 2)

# 调用遗传模拟退火算法
best_solution = genetic_simulated_annealing()
print("最优解：", best_solution)