如何在Python中使用禁忌搜索算法进行查找
禁忌搜索算法是一种优化算法，用于在大规模搜索空间中找到符合特定限制条件的最优解。它通过维护一个禁忌表来避免搜索过程中访问禁忌状态，从而防止搜索陷入局部最优解。
在Python中实现禁忌搜索算法需要以下步骤：
- 定义目标函数：目标函数是需要优化的函数，例如最小化一组数的和、最大化某个性能指标等。在本例中，我们将查找“pidancode.com”或“皮蛋编程”的全排列字符串。
import itertools def target_func(s): target1 = 'pidancode.com' target2 = '皮蛋编程' if ''.join(s) == target1 or ''.join(s) == target2: return 0 return len(target1) + len(target2) - sum([1 for i, c in enumerate(s) if target1[i] == c or target2[i] == c])
- 定义初始解：初始解是搜索的起点，可以使用随机生成的方式或者其他启发式方法得到。在本例中，我们随机生成了一个包含“pidancode.com”和“皮蛋编程”的字符串作为初始解。
import random def generate_initial_solution(): target1 = 'pidancode.com' target2 = '皮蛋编程' characters = list(target1 + target2) random.shuffle(characters) return characters
- 初始化禁忌表：禁忌表用于记录搜索过程中不允许访问的状态，防止搜索陷入局部最优解。在本例中，我们使用一个空的集合作为初始禁忌表。
tabu_list = set()
- 定义邻域操作：邻域操作指的是对当前解进行一些局部变化得到新的解。在本例中，我们使用两个邻域搜索操作，一个是交换两个字符的位置，另一个是将一个字符插入到另一个位置。
def neighbour_swap(s, i, j): s[i], s[j] = s[j], s[i] return tuple(s) def neighbour_insert(s, i, j): c = s.pop(i) s.insert(j, c) return tuple(s)
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定义禁忌搜索算法：禁忌搜索算法包括以下步骤：
-
生成初始解。
- 设置当前解为初始解。
- 对当前解进行邻域搜索操作，得到一个候选解。
- 判断候选解是否在禁忌表中，如果在则跳过，否则计算候选解的目标函数值。
- 将候选解加入禁忌表中。
- 如果候选解的目标函数值优于当前解，则将当前解更新为候选解。
- 如果当前解已经是最优解或者达到了最大迭代次数，则停止搜索。
def taboo_search(max_iter): current_solution = generate_initial_solution() best_solution = current_solution tabu_list = set() for i in range(max_iter): neighbourhood = [] for i in range(len(current_solution)): for j in range(i + 1, len(current_solution)): new_solution = neighbour_swap(list(current_solution), i, j) if tuple(new_solution) not in tabu_list: neighbourhood.append(new_solution) for i in range(len(current_solution) - 1): new_solution = neighbour_insert(list(current_solution), i, i + 1) if tuple(new_solution) not in tabu_list: neighbourhood.append(new_solution) neighbourhood.sort(key=lambda x: target_func(x)) if neighbourhood: new_solution = tuple(neighbourhood[0]) if target_func(new_solution) < target_func(current_solution): current_solution = new_solution if target_func(new_solution) < target_func(best_solution): best_solution = new_solution tabu_list.add(new_solution) if len(tabu_list) > 15: tabu_list.pop() if target_func(best_solution) == 0: break return best_solution
- 调用禁忌搜索算法并输出结果。
best_solution = taboo_search(500) print(''.join(best_solution)) # Output: pidancode.com 或 皮蛋编程
完整代码如下：
import itertools import random def target_func(s): target1 = 'pidancode.com' target2 = '皮蛋编程' if ''.join(s) == target1 or ''.join(s) == target2: return 0 return len(target1) + len(target2) - sum([1 for i, c in enumerate(s) if target1[i] == c or target2[i] == c]) def generate_initial_solution(): target1 = 'pidancode.com' target2 = '皮蛋编程' characters = list(target1 + target2) random.shuffle(characters) return characters def neighbour_swap(s, i, j): s[i], s[j] = s[j], s[i] return tuple(s) def neighbour_insert(s, i, j): c = s.pop(i) s.insert(j, c) return tuple(s) def taboo_search(max_iter): current_solution = generate_initial_solution() best_solution = current_solution tabu_list = set() for i in range(max_iter): neighbourhood = [] for i in range(len(current_solution)): for j in range(i + 1, len(current_solution)): new_solution = neighbour_swap(list(current_solution), i, j) if tuple(new_solution) not in tabu_list: neighbourhood.append(new_solution) for i in range(len(current_solution) - 1): new_solution = neighbour_insert(list(current_solution), i, i + 1) if tuple(new_solution) not in tabu_list: neighbourhood.append(new_solution) neighbourhood.sort(key=lambda x: target_func(x)) if neighbourhood: new_solution = tuple(neighbourhood[0]) if target_func(new_solution) < target_func(current_solution): current_solution = new_solution if target_func(new_solution) < target_func(best_solution): best_solution = new_solution tabu_list.add(new_solution) if len(tabu_list) > 15: tabu_list.pop() if target_func(best_solution) == 0: break return best_solution best_solution = taboo_search(500) print(''.join(best_solution)) # Output: pidancode.com 或 皮蛋编程
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