java深度搜索与广度优先搜索

图结构展示：

 

 

实现过程：

首先，我们来看看图结构在代码中的实现。有三块逻辑：

1.图中的节点：

 

 

public class GraphNode {

    public List edgeList = null;

    private String label = "";

    public GraphNode(String label) {
        this.label = label;
        if (edgeList == null) {
            edgeList = new ArrayList();
        }
    }

    /**
     * 给当前节点添加一条边
     * GraphNode
     * @param edge
     *          添加的边
     */
    public void addEdgeList(GraphEdge edge) {
        edgeList.add(edge);
    }

    public String getLabel() {
        return label;
    }
}

 

2.图中的边：

 

 

public class GraphEdge {

    private GraphNode nodeLeft;

    private GraphNode nodeRight;

    /**
     * @param nodeLeft
     *          边的左端
     * @param nodeRight
     *          边的右端
     */
    public GraphEdge(GraphNode nodeLeft, GraphNode nodeRight) {
        this.nodeLeft = nodeLeft;
        this.nodeRight = nodeRight;
    }

    public GraphNode getNodeLeft() {
        return nodeLeft;
    }

    public GraphNode getNodeRight() {
        return nodeRight;
    }

}

 

3.把节点和边组合成一个图结构：

 

 

public class MyGraph {

    private List nodes = null;

    public void initGraph(int n) {
        if (nodes == null) {
            nodes = new ArrayList();
        }

        GraphNode node = null;
        for (int i = 0; i < n; i++) {
            node = new GraphNode(String.valueOf(i));
            nodes.add(node);
        }
    }

    public void initGraph(int n, boolean b) {
        initGraph(n);
        GraphEdge edge01 = new GraphEdge(nodes.get(0), nodes.get(1));
        GraphEdge edge02 = new GraphEdge(nodes.get(0), nodes.get(2));
        GraphEdge edge13 = new GraphEdge(nodes.get(1), nodes.get(3));
        GraphEdge edge14 = new GraphEdge(nodes.get(1), nodes.get(4));
        GraphEdge edge25 = new GraphEdge(nodes.get(2), nodes.get(5));
        GraphEdge edge26 = new GraphEdge(nodes.get(2), nodes.get(6));
        GraphEdge edge37 = new GraphEdge(nodes.get(3), nodes.get(7));
        GraphEdge edge47 = new GraphEdge(nodes.get(4), nodes.get(7));
        GraphEdge edge56 = new GraphEdge(nodes.get(5), nodes.get(6));


        nodes.get(0).addEdgeList(edge01);
        nodes.get(0).addEdgeList(edge02);
        nodes.get(1).addEdgeList(edge13);
        nodes.get(1).addEdgeList(edge14);
        nodes.get(2).addEdgeList(edge25);
        nodes.get(2).addEdgeList(edge26);
        nodes.get(3).addEdgeList(edge37);
        nodes.get(4).addEdgeList(edge47);
        nodes.get(5).addEdgeList(edge56);
    }

    public void initGraph() {
        initGraph(8, false);
    }

    public List getGraphNodes() {
        return nodes;
    }
}

 
有了图的结构，我们就可以进行一些实际的操作了。

深度优先搜索：

 

 

public class DFSearch {

    /**
     * 深度遍历
     * DFSearch
     * @param node
     *          当前节点
     * @param visited
     *          被访问过的节点列表
     */
    public void searchTraversing(GraphNode node, List visited) {
        // 判断是否遍历过
        if (visited.contains(node)) {
            return;
        }

        visited.add(node);
        System.out.println("节点：" + node.getLabel());
        for (int i = 0; i < node.edgeList.size(); i++) {
            searchTraversing(node.edgeList.get(i).getNodeRight(), visited);
        }
    }
}

 

广度优先搜索：

 

 

public class BFSearch {

    /**
     * 广度优先搜索
     * BFSearch
     * @param node
     *          搜索的入口节点
     */
    public void searchTraversing(GraphNode node) {
        List visited = new ArrayList(); // 已经被访问过的元素
        Queue q = new LinkedList(); // 用队列存放依次要遍历的元素
        q.offer(node);

        while (!q.isEmpty()) {
            GraphNode currNode = q.poll();
            if (!visited.contains(currNode)) {
                visited.add(currNode);
                System.out.println("节点：" + currNode.getLabel());
                for (int i = 0; i < currNode.edgeList.size(); i++) {
                    q.offer(currNode.edgeList.get(i).getNodeRight());
                }
            }
        }
    }
}

 

运行结果：