禁忌搜索算法总成本python实现

禁忌搜索算法是一种启发式搜索算法，用于在大规模搜索空间中寻找最优解。它通过维护一个禁忌表来避免搜索过程中陷入局部最优解，从而提高搜索效率。

禁忌搜索算法的总成本包括两个方面：时间复杂度和空间复杂度。时间复杂度取决于搜索空间的大小和禁忌表的大小，通常情况下是指数级别的。空间复杂度则取决于禁忌表的大小和搜索过程中需要存储的其他信息，通常情况下也是指数级别的。

以下是一个简单的禁忌搜索算法的Python实现：

def tabu_search(initial_solution, neighborhood_func, aspiration_criteria, tabu_list_size, max_iterations):
    current_solution = initial_solution
    best_solution = initial_solution
    tabu_list = []
    iteration = 0
    
    while iteration < max_iterations:
        iteration += 1
        neighborhood = neighborhood_func(current_solution)
        best_neighbor = None
        for neighbor in neighborhood:
            if neighbor not in tabu_list or aspiration_criteria(neighbor):
                if best_neighbor is None or neighbor.cost < best_neighbor.cost:
                    best_neighbor = neighbor
        current_solution = best_neighbor
        if current_solution.cost < best_solution.cost:
            best_solution = current_solution
        tabu_list.append(current_solution)
        if len(tabu_list) > tabu_list_size:
            tabu_list.pop(0)
    
    return best_solution

其中，`initial_solution`是初始解，`neighborhood_func`是产生邻居解的函数，`aspiration_criteria`是采用折衷策略时的判断条件，`tabu_list_size`是禁忌表的大小，`max_iterations`是最大迭代次数。算法返回最优解。