蛇优化算法python代码

以下是蛇优化算法的 Python 实现：

import random
import numpy as np

# 蛇优化算法
def snake_algorithm(objective_function, lb, ub, dimension, iterations, population_size, c1=1, c2=1, c3=1):
    # 初始化种群
    population = np.zeros((population_size, dimension))
    for i in range(population_size):
        population[i, :] = np.array([random.uniform(lb, ub) for j in range(dimension)])
    # 初始化个体最优位置
    pbest = np.copy(population)
    # 初始化个体最优适应度值
    pbest_fitness = np.array([float("inf") for i in range(population_size)])
    # 寻找全局最优位置和适应度值
    gbest_fitness = float("inf")
    gbest = np.zeros(dimension)
    for i in range(iterations):
        for j in range(population_size):
            # 计算当前个体适应度值
            fitness = objective_function(population[j, :])
            # 更新个体最优位置和适应度值
            if fitness < pbest_fitness[j]:
                pbest[j, :] = np.copy(population[j, :])
                pbest_fitness[j] = fitness
            # 更新全局最优位置和适应度值
            if fitness < gbest_fitness:
                gbest = np.copy(population[j, :])
                gbest_fitness = fitness
        # 更新种群位置
        for j in range(population_size):
            r1 = random.random()
            r2 = random.random()
            r3 = random.random()
            population[j, :] = population[j, :] + c1 * r1 * (pbest[j, :] - population[j, :]) + c2 * r2 * (gbest - population[j, :]) + c3 * r3 * (np.mean(population, axis=0) - population[j, :])
            # 确保位置在搜索空间内
            population[j, :] = np.maximum(population[j, :], lb)
            population[j, :] = np.minimum(population[j, :], ub)
    return gbest, gbest_fitness

其中，`objective_function` 是目标函数，`lb` 和 `ub` 是搜索空间的下界和上界，`dimension` 是搜索空间的维度，`iterations` 是迭代次数，`population_size` 是种群大小，`c1`、`c2` 和 `c3` 是三个常数。算法返回全局最优位置和适应度值。