人工蜂群算法代码

以下是一个简单的 Python 实现人工蜂群算法的示例代码：

import random
import math

class Bee:
    def __init__(self, pos, func):
        self.pos = pos
        self.func = func
        self.value = self.func(self.pos)

class ABC:
    def __init__(self, func, n_bees=10, n_iter=100):
        self.func = func
        self.n_bees = n_bees
        self.n_iter = n_iter
        self.bees = []
        self.best_bee = None
        self.__generate_bees()

    def __generate_bees(self):
        for i in range(self.n_bees):
            pos = [random.uniform(-10, 10) for i in range(self.func.__code__.co_argcount)]
            bee = Bee(pos, self.func)
            self.bees.append(bee)
            if self.best_bee is None or bee.value < self.best_bee.value:
                self.best_bee = bee

    def __search_neighborhood(self, current_bee):
        phi = random.uniform(-1, 1)
        index = random.randint(0, self.n_bees - 1)
        neighbor_bee = self.bees[index]
        new_pos = [current_bee.pos[i] + phi * (current_bee.pos[i] - neighbor_bee.pos[i])
                   for i in range(len(current_bee.pos))]
        return Bee(new_pos, self.func)

    def optimize(self):
        for i in range(self.n_iter):
            for j in range(self.n_bees):
                current_bee = self.bees[j]
                neighbor_bee = self.__search_neighborhood(current_bee)
                if neighbor_bee.value < current_bee.value:
                    current_bee = neighbor_bee
                if current_bee.value < self.best_bee.value:
                    self.best_bee = current_bee
            self.__update_bees()

    def __update_bees(self):
        values = [bee.value for bee in self.bees]
        mean = sum(values) / len(values)
        std_dev = math.sqrt(sum([(val - mean) ** 2 for val in values]) / len(values))
        for bee in self.bees:
            phi = random.uniform(-1, 1)
            new_pos = [bee.pos[i] + phi * std_dev for i in range(len(bee.pos))]
            bee.pos = new_pos
            bee.value = bee.func(bee.pos)
            if bee.value < self.best_bee.value:
                self.best_bee = bee

if __name__ == "__main__":
    def sphere_function(*args):
        return sum([arg ** 2 for arg in args])

    abc = ABC(sphere_function, n_bees=20, n_iter=50)
    abc.optimize()
    print("Best solution found: f({}) = {}".format(abc.best_bee.pos, abc.best_bee.value))

该代码实现了一个人工蜂群算法的类 "ABC"，其中包含一个函数 "optimize()"，该函数在给定的函数空间中执行优化。函数空间由函数 "func" 和其参数个数 "n_bees" 定义。在这个示例中，优化的目标是最小化球形函数。

该算法的优化过程分为两个主要步骤：

- 搜索邻域：每个工蜂都在其邻域内搜索一个新位置，并计算其价值。
- 更新工蜂：每个工蜂都在其当前位置和新位置之间进行随机跳跃，以帮助搜索空间的探索和利用。

最终的优化结果存储在 "best_bee" 中，包括最佳解决方案的位置和价值。