编辑“︁双向搜索”︁（章节）

== 代码实现 ==
以下是Python实现双向BFS的示例（假设图为无向图）：

<syntaxhighlight lang="python">
from collections import deque

def bidirectional_bfs(graph, start, end):
    if start == end:
        return [start]

    # 初始化正向和反向队列及已访问字典
    queue_start = deque([start])
    visited_start = {start: None}
    queue_end = deque([end])
    visited_end = {end: None}

    while queue_start and queue_end:
        # 正向搜索一步
        current_start = queue_start.popleft()
        for neighbor in graph[current_start]:
            if neighbor not in visited_start:
                visited_start[neighbor] = current_start
                queue_start.append(neighbor)
                if neighbor in visited_end:  # 交集检测
                    return reconstruct_path(visited_start, visited_end, neighbor)

        # 反向搜索一步
        current_end = queue_end.popleft()
        for neighbor in graph[current_end]:
            if neighbor not in visited_end:
                visited_end[neighbor] = current_end
                queue_end.append(neighbor)
                if neighbor in visited_start:  # 交集检测
                    return reconstruct_path(visited_start, visited_end, neighbor)

    return None  # 无路径

def reconstruct_path(visited_start, visited_end, meeting_node):
    # 从交会节点向起点回溯
    path = []
    node = meeting_node
    while node is not None:
        path.append(node)
        node = visited_start[node]
    path.reverse()  # 反转得到正向路径

    # 从交会节点向终点回溯（跳过交会节点避免重复）
    node = visited_end[meeting_node]
    while node is not None:
        path.append(node)
        node = visited_end[node]
    return path

# 示例图（无向图用邻接表表示）
graph = {
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E']
}

# 测试
print(bidirectional_bfs(graph, 'A', 'F'))  # 输出: ['A', 'C', 'F']
</syntaxhighlight>

'''输入说明'''：图以邻接表形式存储，起点为`'A'`，终点为`'F'`。<br>
'''输出结果'''：路径`['A', 'C', 'F']`。