python 变邻域搜素算法,包含3个邻域的流程的完整代码
时间: 2024-04-16 09:26:30 浏览: 22
下面是一个使用Python实现包含3个邻域的变邻域搜索算法的完整代码示例:
```python
def neighborhood_search(initial_solution, neighborhoods, evaluation_func, max_iterations):
best_solution = initial_solution
best_evaluation = evaluation_func(initial_solution)
for i in range(max_iterations):
found_better = False
for neighborhood in neighborhoods:
neighbors = neighborhood(best_solution)
for neighbor in neighbors:
neighbor_evaluation = evaluation_func(neighbor)
if neighbor_evaluation < best_evaluation:
best_solution = neighbor
best_evaluation = neighbor_evaluation
found_better = True
if not found_better:
break
return best_solution, best_evaluation
# 示例问题:寻找列表中的最小值
def find_minimum_value(numbers):
return min(numbers)
# 示例问题的邻域生成函数:通过交换列表中的两个元素生成邻居解
def swap_neighborhood(solution):
neighbors = []
n = len(solution)
for i in range(n):
for j in range(i + 1, n):
neighbor = solution[:]
neighbor[i], neighbor[j] = neighbor[j], neighbor[i]
neighbors.append(neighbor)
return neighbors
# 示例问题的评估函数:计算列表中所有元素的和
def sum_evaluation(solution):
return sum(solution)
# 示例问题的其他两个邻域生成函数:通过插入元素和删除元素生成邻居解
def insert_neighborhood(solution):
neighbors = []
n = len(solution)
for i in range(n):
for j in range(n + 1):
neighbor = solution[:]
neighbor.insert(j, i)
neighbors.append(neighbor)
return neighbors
def delete_neighborhood(solution):
neighbors = []
n = len(solution)
for i in range(n):
neighbor = solution[:]
del neighbor[i]
neighbors.append(neighbor)
return neighbors
# 测试代码
numbers = [9, 3, 2, 6, 1, 5, 8, 4, 7]
initial_solution = numbers[:]
max_iterations = 1000
neighborhoods = [swap_neighborhood, insert_neighborhood, delete_neighborhood]
best_solution, best_evaluation = neighborhood_search(initial_solution, neighborhoods, sum_evaluation, max_iterations)
print("Best Solution:", best_solution)
print("Best Evaluation:", best_evaluation)
```
在这个示例代码中,我们定义了一个示例问题:寻找一个列表中的最小值。我们通过变邻域搜索算法来解决这个问题。在 `neighborhood_search` 函数中,我们传入了一个包含三个邻域函数的列表 `neighborhoods`,分别是 `swap_neighborhood`、`insert_neighborhood` 和 `delete_neighborhood`。
我们使用一个简单的列表作为示例问题的输入,然后调用 `neighborhood_search` 函数来搜索最优解。最后打印出找到的最优解和对应的评估值。
请注意,这只是一个示例,你可以根据具体的问题定义自己的邻域生成函数和评估函数。同时,你可以根据需求添加更多的邻域函数来增加搜索的多样性和广度。
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