用Java实现带有重新计算路径的ImprovePath函数的Anytime repair Astar算法

好的，下面是Java实现带有重新计算路径的ImprovePath函数的Anytime repair Astar算法的代码：

import java.util.*;

public class AnytimeRepairAStar {
    private final int[][] grid;
    private final int n;
    private final int m;
    private final int startX;
    private final int startY;
    private final int goalX;
    private final int goalY;

    public AnytimeRepairAStar(int[][] grid, int startX, int startY, int goalX, int goalY) {
        this.grid = grid;
        this.n = grid.length;
        this.m = grid[0].length;
        this.startX = startX;
        this.startY = startY;
        this.goalX = goalX;
        this.goalY = goalY;
    }

    public List<int[]> findPath() {
        PriorityQueue<Node> openSet = new PriorityQueue<>();
        Map<Node, Node> cameFrom = new HashMap<>();
        Map<Node, Integer> gScore = new HashMap<>();
        Map<Node, Integer> fScore = new HashMap<>();

        Node start = new Node(startX, startY);
        Node goal = new Node(goalX, goalY);

        gScore.put(start, 0);
        fScore.put(start, heuristic(start, goal));
        openSet.add(start);

        while (!openSet.isEmpty()) {
            Node current = openSet.poll();
            if (current.equals(goal)) {
                return reconstructPath(cameFrom, current);
            }
            for (Node neighbor : getNeighbors(current)) {
                int tentativeGScore = gScore.get(current) + getDistance(current, neighbor);
                if (!gScore.containsKey(neighbor) || tentativeGScore < gScore.get(neighbor)) {
                    cameFrom.put(neighbor, current);
                    gScore.put(neighbor, tentativeGScore);
                    fScore.put(neighbor, tentativeGScore + heuristic(neighbor, goal));
                    openSet.add(neighbor);
                }
            }

            if (openSet.isEmpty()) {
                break;
            }

            Node bestNode = openSet.peek();
            if (fScore.get(bestNode) >= fScore.get(current)) {
                break;
            }

            Node prevBestNode = null;
            while (!openSet.isEmpty()) {
                Node node = openSet.poll();
                if (node.equals(bestNode)) {
                    break;
                }
                prevBestNode = node;
            }

            if (prevBestNode != null) {
                openSet.add(prevBestNode);
            }

            List<Node> path = reconstructPath(cameFrom, current);
            for (int i = 0; i < path.size() - 1; i++) {
                Node node1 = path.get(i);
                Node node2 = path.get(i + 1);
                int distance = getDistance(node1, node2);
                int newGScore = gScore.get(node1) + distance;
                if (!gScore.containsKey(node2) || newGScore < gScore.get(node2)) {
                    cameFrom.put(node2, node1);
                    gScore.put(node2, newGScore);
                    fScore.put(node2, newGScore + heuristic(node2, goal));
                }
            }
        }

        return null;
    }

    private int getDistance(Node node1, Node node2) {
        return Math.abs(node1.x - node2.x) + Math.abs(node1.y - node2.y);
    }

    private List<Node> getNeighbors(Node node) {
        List<Node> neighbors = new ArrayList<>();
        if (node.x > 0 && grid[node.x - 1][node.y] != -1) {
            neighbors.add(new Node(node.x - 1, node.y));
        }
        if (node.x < n - 1 && grid[node.x + 1][node.y] != -1) {
            neighbors.add(new Node(node.x + 1, node.y));
        }
        if (node.y > 0 && grid[node.x][node.y - 1] != -1) {
            neighbors.add(new Node(node.x, node.y - 1));
        }
        if (node.y < m - 1 && grid[node.x][node.y + 1] != -1) {
            neighbors.add(new Node(node.x, node.y + 1));
        }
        return neighbors;
    }

    private int heuristic(Node node1, Node node2) {
        return Math.abs(node1.x - node2.x) + Math.abs(node1.y - node2.y);
    }

    private List<int[]> reconstructPath(Map<Node, Node> cameFrom, Node current) {
        List<int[]> path = new ArrayList<>();
        while (cameFrom.containsKey(current)) {
            path.add(new int[]{current.x, current.y});
            current = cameFrom.get(current);
        }
        path.add(new int[]{current.x, current.y});
        Collections.reverse(path);
        return path;
    }

    private static class Node implements Comparable<Node> {
        private final int x;
        private final int y;

        public Node(int x, int y) {
            this.x = x;
            this.y = y;
        }

        @Override
        public boolean equals(Object o) {
            if (this == o) return true;
            if (o == null || getClass() != o.getClass()) return false;
            Node node = (Node) o;
            return x == node.x && y == node.y;
        }

        @Override
        public int hashCode() {
            return Objects.hash(x, y);
        }

        @Override
        public int compareTo(Node other) {
            return Integer.compare(this.fScore(), other.fScore());
        }

        public int fScore() {
            return 0;
        }
    }
}

在这个实现中，我们使用了优先队列来维护当前可用的节点，以及使用了哈希表来存储节点之间的关系。我们还实现了getNeighbors函数来获取节点的邻居，以及heuristic函数来计算节点到目标节点的估价函数。

在findPath函数中，我们首先初始化起始节点和目标节点，并将起始节点加入到优先队列中。然后，我们开始循环，直到找到最优解或者无法再找到更好的解为止。在每一次循环中，我们从优先队列中取出fScore值最小的节点，并检查该节点是否为目标节点。如果是，我们就通过reconstructPath函数重构路径并返回。否则，我们遍历当前节点的邻居，计算新的gScore和fScore值，并将其加入优先队列中。

如果优先队列为空，我们就跳出循环。否则，我们查找优先队列中fScore值最小的节点，并将其与当前节点进行比较。如果当前节点的fScore值小于等于最小值，就跳出循环。否则，我们从优先队列中取出节点，直到找到最小值为止，并将其重新加入优先队列中。接下来，我们使用reconstructPath函数重构路径，并对路径中所有节点重新计算gScore和fScore值。最后，我们继续循环，直到找到最优解或者无法再找到更好的解为止。

这样就完成了带有重新计算路径的ImprovePath函数的Anytime repair Astar算法的Java实现。