禁忌算法python课程设计
时间: 2023-07-10 12:39:22 浏览: 52
禁忌搜索算法是一种基于启发式的优化算法,通常用于求解NP难问题。它采用一种策略来避免搜寻状态空间中的局部最优解,从而找到全局最优解。在禁忌搜索算法中,我们使用一个禁忌表来记录一些已经访问过的状态,以避免在接下来的搜索中继续访问这些状态,从而加速搜索过程。下面是一个简单的禁忌搜索算法的Python实现:
```python
def tabu_search(initial_solution, tabu_list_length, neighborhood_size, num_iterations):
current_solution = initial_solution
best_solution = initial_solution
tabu_list = []
for i in range(num_iterations):
neighborhood = get_neighborhood(current_solution, neighborhood_size)
best_neighbor = None
best_neighbor_cost = float('inf')
for neighbor in neighborhood:
if neighbor not in tabu_list:
cost = evaluate_solution(neighbor)
if cost < best_neighbor_cost:
best_neighbor = neighbor
best_neighbor_cost = cost
if best_neighbor is None:
break
current_solution = best_neighbor
if best_neighbor_cost < evaluate_solution(best_solution):
best_solution = best_neighbor
tabu_list.append(best_neighbor)
if len(tabu_list) > tabu_list_length:
tabu_list.pop(0)
return best_solution
```
以上代码中,`initial_solution`是初始解,`tabu_list_length`是禁忌表的长度,`neighborhood_size`是每次搜索时考虑的邻域大小,`num_iterations`是搜索的迭代次数。`get_neighborhood`函数用于获取当前解的邻域,`evaluate_solution`函数用于评估解的质量。在算法的主循环中,我们从邻域中选择一个未被禁忌的邻居,并将其作为当前解。如果该邻居比当前最优解更好,则更新最优解。同时,我们将该邻居添加到禁忌表中,并限制禁忌表的长度不超过指定值。
你可以根据自己的需要来修改和扩展以上代码,以满足具体问题的求解需求。