乌鸦搜索算法python代码

以下是一个简单的乌鸦搜索算法的Python代码实现：

import math

def crow_search(fitness_func, lower_bound, upper_bound, num_iterations, num_crows, alpha, beta, delta):
    # 初始化乌鸦们
    crows = []
    for i in range(num_crows):
        crow = {
            "position": [0.0] * len(lower_bound),
            "fitness": math.inf
        }
        for j in range(len(lower_bound)):
            crow["position"][j] = lower_bound[j] + (upper_bound[j] - lower_bound[j]) * random.random()
        crow["fitness"] = fitness_func(crow["position"])
        crows.append(crow)
    
    # 迭代寻找最优解
    for t in range(num_iterations):
        # 计算每个乌鸦的适应度值
        for i in range(num_crows):
            crows[i]["fitness"] = fitness_func(crows[i]["position"])
        
        # 对乌鸦按适应度值排序
        crows.sort(key=lambda x: x["fitness"])
        
        # 更新最优解
        best_crow = crows[0]
        
        # 移动乌鸦
        for i in range(num_crows):
            for j in range(len(lower_bound)):
                # 计算乌鸦的新位置
                r1 = random.random()
                r2 = random.random()
                r3 = random.random()
                crows[i]["position"][j] = best_crow["position"][j] + alpha * (math.exp(-beta * r1) - math.exp(-beta)) * (best_crow["position"][j] - delta * crows[i]["position"][j]) + alpha * (math.exp(-beta * r2) - math.exp(-beta)) * (crows[i]["position"][j] - delta * crows[i]["position"][j]) + alpha * (math.exp(-beta * r3) - math.exp(-beta)) * (upper_bound[j] - lower_bound[j]) * random.random()
                
                # 确保新位置在可行域内
                crows[i]["position"][j] = max(lower_bound[j], min(upper_bound[j], crows[i]["position"][j]))
            
            # 更新适应度值
            crows[i]["fitness"] = fitness_func(crows[i]["position"])
    
    # 返回最优解
    crows.sort(key=lambda x: x["fitness"])
    return crows[0]["position"]

其中，`fitness_func` 是一个用于计算适应度值的函数，`lower_bound` 和 `upper_bound` 分别是每个变量的下界和上界，`num_iterations` 是迭代次数，`num_crows` 是乌鸦数量，`alpha`、`beta` 和 `delta` 是算法的超参数。