人工蜂群算法python

好的，以下是一个简单的人工蜂群算法的 Python 代码示例：

import numpy as np

# 蜜蜂类
class Bee:
    def __init__(self, pos, val):
        self.pos = pos  # 蜜蜂位置
        self.val = val  # 蜜蜂价值

# 生成随机初始蜜蜂群
def generate_bees(num_bees, num_params, lb, ub):
    bees = []
    for i in range(num_bees):
        pos = lb + (ub - lb) * np.random.rand(num_params)
        val = evaluate(pos)
        bee = Bee(pos, val)
        bees.append(bee)
    return bees

# 计算蜜蜂的适应度值
def evaluate(pos):
    val = np.sum(pos ** 2)
    return val

# 更新蜜蜂位置
def update_bee_pos(bee, patch_size, lb, ub):
    new_pos = bee.pos + patch_size * (2 * np.random.rand(len(bee.pos)) - 1)
    new_pos = np.clip(new_pos, lb, ub)
    new_val = evaluate(new_pos)
    if new_val < bee.val:
        bee.pos = new_pos
        bee.val = new_val

# 执行人工蜂群算法
def abc(num_bees, num_params, max_iter, patch_size):
    lb = -5
    ub = 5
    bees = generate_bees(num_bees, num_params, lb, ub)
    best_val = float('inf')
    best_pos = None
    for i in range(max_iter):
        for j in range(num_bees):
            if j % 3 == 0:
                k = np.random.randint(num_bees)
                while k == j:
                    k = np.random.randint(num_bees)
                if bees[j].val < bees[k].val:
                    update_bee_pos(bees[j], patch_size, lb, ub)
            elif j % 3 == 1:
                k = np.random.randint(num_bees)
                while k == j:
                    k = np.random.randint(num_bees)
                if bees[j].val < bees[k].val:
                    update_bee_pos(bees[j], patch_size, lb, ub)
                else:
                    k = np.random.randint(num_bees)
                    while k == j:
                        k = np.random.randint(num_bees)
                    if bees[j].val < bees[k].val:
                        update_bee_pos(bees[j], patch_size, lb, ub)
            else:
                k1 = np.random.randint(num_bees)
                while k1 == j:
                    k1 = np.random.randint(num_bees)
                k2 = np.random.randint(num_bees)
                while k2 == j or k2 == k1:
                    k2 = np.random.randint(num_bees)
                phi = np.random.rand()
                new_pos = bees[j].pos + phi * (bees[k1].pos - bees[k2].pos)
                new_pos = np.clip(new_pos, lb, ub)
                new_val = evaluate(new_pos)
                if new_val < bees[j].val:
                    bees[j].pos = new_pos
                    bees[j].val = new_val
        for bee in bees:
            if bee.val < best_val:
                best_val = bee.val
                best_pos = bee.pos
        print('Iteration:', i+1, ' Best Value:', best_val)
    return best_val, best_pos

注意，这只是一个简单的示例，实际运行效果可能需要根据具体问题进行调整。