用Python写一个狼群算法解决tsp问题的完整代码

以下是用Python编写狼群算法解决TSP问题的完整代码：

import numpy as np
import random

# 定义城市坐标
CITY_POSITIONS = np.array([[60, 200], [180, 200], [80, 180], [140, 180], [20, 160], [100, 160], [200, 160], [140, 140], [40, 120], [100, 120], [180, 100], [60, 80], [120, 80], [180, 60], [20, 40], [100, 40], [200, 40], [20, 20], [60, 20], [160, 20]])

# 常量定义
ALPHA = 0.5
BETA = 0.8
DELTA = 1
N_POP = 10
N_ITERS = 100
N_CITIES = CITY_POSITIONS.shape[0]
DISTANCES = np.zeros((N_CITIES, N_CITIES))
for i in range(N_CITIES):
    for j in range(i + 1, N_CITIES):
        DISTANCES[i][j] = np.sqrt(np.sum((CITY_POSITIONS[i] - CITY_POSITIONS[j]) ** 2))
        DISTANCES[j][i] = DISTANCES[i][j]

# 初始化狼群
class Wolf:
    def __init__(self):
        self.position = np.random.permutation(N_CITIES)
        self.fitness = self.evaluate_fitness()

    def evaluate_fitness(self):
        return sum([DISTANCES[self.position[i - 1]][self.position[i]] for i in range(N_CITIES)]) + DISTANCES[self.position[-1]][self.position[0]]

wolves = [Wolf() for i in range(N_POP)]

# 迭代优化
for i in range(N_ITERS):
    # 排序
    wolves = sorted(wolves, key=lambda x: x.fitness)

    # 更新最优解
    best_wolf = wolves[0]
    best_fitness = best_wolf.fitness

    # 更新每只狼的位置
    for wolf in wolves:
        # 狼最优解向量
        x1 = best_wolf.position
        # 狼当前解向量
        x2 = wolf.position
        # 狼随机解向量
        x3 = wolves[random.randint(0, N_POP - 1)].position

        # 狼位置更新
        wolf.position = np.array([x2[i] + ALPHA * (x1[i] - x2[i]) + BETA * (x3[i] - x2[i]) for i in range(N_CITIES)])

        # 边界处理
        wolf.position = np.array([np.clip(wolf.position[i], 0, N_CITIES - 1) for i in range(N_CITIES)])

        # 评估适应度
        wolf.fitness = wolf.evaluate_fitness()

    # 挑选最优解
    wolves = sorted(wolves, key=lambda x: x.fitness)
    wolves = wolves[:DELTA]
    
    # 输出最优解
    print('Iteration:', i, 'Best Fitness:', best_fitness, 'Best Solution:', best_wolf.position)

代码中使用了numpy库进行矩阵计算和边界处理。代码中定义了一个Wolf类，表示狼，其中包含了狼的位置和适应度。首先对狼群进行初始化，然后进行迭代优化。每次迭代，首先对狼群按适应度进行排序，然后更新最优解，再更新每只狼的位置。更新位置时，每只狼会受到最优解向量、当前解向量和随机解向量的影响，其中ALPHA和BETA是控制影响程度的参数。更新位置后，对边界进行处理，再评估适应度。更新完所有狼的位置后，再挑选最优解，删除其他狼，输出最优解。