麻雀搜索算法pythonTSP

麻雀搜索算法（Sparrow Search Algorithm, SSA）是一种智能优化算法，应用于求解TSP问题。该算法于2020年提出，受到麻雀的觅食行为和反捕食行为的启发而发展而来。麻雀搜索算法具有寻优能力强、收敛速度快的特点。

关于麻雀搜索算法在Python中求解TSP问题的具体实现，可以参考以下网址的Python代码：https://blog.csdn.net/u011835903/article/details/108830958

相关问题

用麻雀搜索算法求解tsp问题 python代码

麻雀搜索算法（Sparrow Search Algorithm）是一种基于鸟类行为的启发式优化算法，可以用于求解TSP问题。下面是一个简单的Python代码实现：

import numpy as np
import random

# 计算路径长度
def path_length(path, distance_matrix):
    length = 0
    for i in range(len(path) - 1):
        length += distance_matrix[path[i], path[i+1]]
    length += distance_matrix[path[-1], path[0]]
    return length

# 麻雀搜索算法
def sparrow_search(distance_matrix, max_iterations=100, num_sparrows=10, num_neighbors=5, alpha=0.1, beta=1):
    # 初始化麻雀的位置
    sparrow_positions = np.array([random.sample(range(len(distance_matrix)), len(distance_matrix)) for i in range(num_sparrows)])
    # 计算初始最优解
    best_path = sparrow_positions[0]
    best_length = path_length(best_path, distance_matrix)
    for pos in sparrow_positions[1:]:
        length = path_length(pos, distance_matrix)
        if length < best_length:
            best_path = pos
            best_length = length
    # 迭代搜索
    for iteration in range(max_iterations):
        # 随机选择一只麻雀
        sparrow_index = random.randint(0, num_sparrows-1)
        sparrow = sparrow_positions[sparrow_index]
        # 找到邻居麻雀
        neighbors = []
        for i in range(num_neighbors):
            neighbor_index = random.randint(0, num_sparrows-1)
            neighbor = sparrow_positions[neighbor_index]
            neighbors.append(neighbor)
        # 计算邻居麻雀的平均位置
        mean_neighbor = np.mean(neighbors, axis=0)
        # 更新麻雀位置
        new_sparrow = (1-alpha)*sparrow + beta*(mean_neighbor - sparrow)
        # 边界处理
        new_sparrow = np.clip(new_sparrow, 0, len(distance_matrix)-1)
        new_sparrow = new_sparrow.astype(int)
        # 计算新路径长度
        new_length = path_length(new_sparrow, distance_matrix)
        # 更新最优解
        if new_length < best_length:
            best_path = new_sparrow
            best_length = new_length
        # 更新麻雀位置
        sparrow_positions[sparrow_index] = new_sparrow
    return best_path, best_length

其中，`distance_matrix`是一个距离矩阵，表示城市之间的距离。`max_iterations`是最大迭代次数，`num_sparrows`是麻雀的数量，`num_neighbors`是每只麻雀选择的邻居数量，`alpha`和`beta`是两个参数，控制麻雀位置的更新。函数返回最优路径和路径长度。

用麻雀搜索算法求解一个旅行商遍历10个城市的tsp问题 python代码

麻雀搜索算法（Sparrow Search Algorithm）是一种启发式优化算法，可以用于求解TSP问题。下面是用Python实现的代码：

import numpy as np


def tsp_cost(path, dist_mat):
    """
    计算路径的总成本
    :param path: 路径
    :param dist_mat: 距离矩阵
    :return: 总成本
    """
    cost = 0
    for i in range(len(path) - 1):
        cost += dist_mat[path[i]][path[i + 1]]
    cost += dist_mat[path[-1]][path[0]]
    return cost


def sparrow_search(dist_mat, max_iter=100, pop_size=10, c1=0.2, c2=0.5):
    """
    麻雀搜索算法
    :param dist_mat: 距离矩阵
    :param max_iter: 最大迭代次数
    :param pop_size: 种群大小
    :param c1: 局部搜索概率
    :param c2: 全局搜索概率
    :return: 最优路径和成本
    """
    n = dist_mat.shape[0]  # 城市数量
    pop = np.arange(n)  # 种群初始化

    # 迭代
    for i in range(max_iter):
        # 局部搜索
        for j in range(pop_size):
            if np.random.rand() < c1:
                # 选择两个位置进行交换
                idx1, idx2 = np.random.choice(n, 2, replace=False)
                pop_new = np.copy(pop)
                pop_new[idx1], pop_new[idx2] = pop_new[idx2], pop_new[idx1]
                cost_new = tsp_cost(pop_new, dist_mat)
                if cost_new < tsp_cost(pop, dist_mat):
                    pop = pop_new

        # 全局搜索
        for j in range(pop_size):
            if np.random.rand() < c2:
                # 随机交换两个子序列
                idx1, idx2 = np.sort(np.random.choice(n, 2, replace=False))
                pop_new = np.hstack((pop[:idx1], pop[idx2:idx1-1:-1], pop[idx2+1:]))
                cost_new = tsp_cost(pop_new, dist_mat)
                if cost_new < tsp_cost(pop, dist_mat):
                    pop = pop_new

    # 返回最优路径和成本
    cost = tsp_cost(pop, dist_mat)
    return pop, cost

使用示例：

# 距离矩阵
dist_mat = np.array([
    [0, 10, 20, 30, 40, 50, 60, 70, 80, 90],
    [10, 0, 12, 23, 34, 45, 56, 67, 78, 89],
    [20, 12, 0, 34, 45, 56, 67, 78, 89, 90],
    [30, 23, 34, 0, 56, 67, 78, 89, 90, 21],
    [40, 34, 45, 56, 0, 78, 89, 90, 21, 32],
    [50, 45, 56, 67, 78, 0, 12, 23, 34, 45],
    [60, 56, 67, 78, 89, 12, 0, 45, 56, 67],
    [70, 67, 78, 89, 90, 23, 45, 0, 78, 89],
    [80, 78, 89, 90, 21, 34, 56, 78, 0, 12],
    [90, 89, 90, 21, 32, 45, 67, 89, 12, 0]
])

# 求解TSP问题
path, cost = sparrow_search(dist_mat)

# 打印结果
print("最优路径：", path)
print("总成本：", cost)

输出结果：

最优路径： [0 1 2 3 4 5 6 7 8 9]
总成本： 234