Python实现狼群算法解决01背包问题的完整代码

好的，以下是Python实现狼群算法解决01背包问题的完整代码：

import random

# 定义狼群算法类
class WolfPackAlgorithm:
    def __init__(self, n, m, w, v, c, p=0.8, a=2):
        self.n = n  # 物品数量
        self.m = m  # 背包容量
        self.w = w  # 物品重量
        self.v = v  # 物品价值
        self.c = c  # 狼群数量
        self.p = p  # 变异概率
        self.a = a  # 狼群半径

    # 初始化狼群
    def init_wolf_pack(self):
        wolf_pack = []
        for i in range(self.c):
            wolf = [random.randint(0, 1) for j in range(self.n)]
            wolf_pack.append(wolf)
        return wolf_pack

    # 计算适应度
    def calc_fitness(self, wolf):
        weight = sum([wolf[i] * self.w[i] for i in range(self.n)])
        if weight > self.m:
            return -1
        else:
            value = sum([wolf[i] * self.v[i] for i in range(self.n)])
            return value

    # 选择狼群中最优秀的狼
    def select_alpha(self, wolf_pack):
        alpha = wolf_pack[0]
        alpha_fitness = self.calc_fitness(alpha)
        for wolf in wolf_pack:
            fitness = self.calc_fitness(wolf)
            if fitness > alpha_fitness:
                alpha = wolf
                alpha_fitness = fitness
        return alpha

    # 更新狼群
    def update_wolf_pack(self, wolf_pack, alpha):
        new_wolf_pack = []
        for wolf in wolf_pack:
            # 狼群半径内没有更优秀的狼，按照原狼的行为进行
            if wolf == alpha:
                new_wolf = wolf.copy()
                for i in range(self.n):
                    if random.random() < 1 / (1 + pow(2.71828, -self.a)):
                        new_wolf[i] = 1 - new_wolf[i]
                new_wolf_pack.append(new_wolf)
            # 狼群半径内存在更优秀的狼，按照更优秀的狼的行为进行
            else:
                new_wolf = wolf.copy()
                for i in range(self.n):
                    if alpha[i] != wolf[i] and random.random() < 1 / (1 + pow(2.71828, -self.a)):
                        new_wolf[i] = alpha[i]
                new_wolf_pack.append(new_wolf)
        return new_wolf_pack

    # 狼群算法求解01背包问题
    def solve_knapsack_problem(self):
        wolf_pack = self.init_wolf_pack()
        for i in range(100):
            alpha = self.select_alpha(wolf_pack)
            new_wolf_pack = self.update_wolf_pack(wolf_pack, alpha)
            for j in range(self.c):
                if random.random() < self.p:
                    for k in range(self.n):
                        if random.random() < 0.5:
                            new_wolf_pack[j][k] = 1 - new_wolf_pack[j][k]
            wolf_pack = new_wolf_pack
        alpha = self.select_alpha(wolf_pack)
        weight = sum([alpha[i] * self.w[i] for i in range(self.n)])
        value = sum([alpha[i] * self.v[i] for i in range(self.n)])
        return alpha, weight, value

# 测试代码
if __name__ == '__main__':
    n = 10
    m = 50
    w = [random.randint(1, 10) for i in range(n)]
    v = [random.randint(10, 100) for i in range(n)]
    c = 20
    wolf_pack_algorithm = WolfPackAlgorithm(n, m, w, v, c)
    alpha, weight, value = wolf_pack_algorithm.solve_knapsack_problem()
    print('最优解：', alpha)
    print('重量：', weight)
    print('价值：', value)

在上述代码中，我们定义了一个`WolfPackAlgorithm`类，其中包括初始化狼群、计算适应度、选择最优秀的狼、更新狼群等方法。最后，我们进行了测试，使用狼群算法求解了一个10个物品，背包容量为50的01背包问题，并输出了最优解、重量和价值。