37：最优执行方案规划：A*搜索与多目标优化

class MultiObjectiveAStar:
    def __init__(self, weights):
        self.weights = weights  # 各目标的权重
    
    def search(self, start, goal, graph):
        """多目标A*搜索"""
        open_list = []
        closed_list = set()
        
        # 初始化起点
        start_node = {
            'position': start,
            'g': {k: 0 for k in self.weights.keys()},
            'h': self._calculate_heuristics(start, goal),
            'parent': None
        }
        
        # 计算综合f值
        start_node['f'] = self._calculate_f(start_node)
        
        # 添加到开放列表
        heapq.heappush(open_list, (start_node['f'], start_node))
        
        while open_list:
            # 选择f值最小的节点
            current_f, current_node = heapq.heappop(open_list)
            
            # 到达目标
            if current_node['position'] == goal:
                return self._reconstruct_path(current_node)
            
            # 加入封闭列表
            closed_list.add(current_node['position'])
            
            # 生成后继节点
            for neighbor in graph.get_neighbors(current_node['position']):
                if neighbor in closed_list:
                    continue
                
                # 计算各目标的g值
                neighbor_g = {}
                for k in self.weights.keys():
                    neighbor_g[k] = current_node['g'][k] + graph.get_cost(current_node['position'], neighbor, k)
                
                # 计算启发值
                neighbor_h = self._calculate_heuristics(neighbor, goal)
                
                # 创建后继节点
                neighbor_node = {
                    'position': neighbor,
                    'g': neighbor_g,
                    'h': neighbor_h,
                    'parent': current_node
                }
                neighbor_node['f'] = self._calculate_f(neighbor_node)
                
                # 检查是否已在开放列表中
                in_open = False
                for i, (f, node) in enumerate(open_list):
                    if node['position'] == neighbor:
                        if neighbor_node['f'] < f:
                            # 更新开放列表中的节点
                            open_list[i] = (neighbor_node['f'], neighbor_node)
                            heapq.heapify(open_list)
                        in_open = True
                        break
                
                if not in_open:
                    heapq.heappush(open_list, (neighbor_node['f'], neighbor_node))
        
        return None  # 无路径
    
    def _calculate_heuristics(self, position, goal):
        """计算启发值"""
        heuristics = {}
        for k in self.weights.keys():
            # 根据不同目标计算启发值
            if k == 'distance':
                heuristics[k] = self._euclidean_distance(position, goal)
            elif k == 'time':
                heuristics[k] = self._time_estimate(position, goal)
            elif k == 'risk':
                heuristics[k] = self._risk_estimate(position, goal)
        return heuristics
    
    def _calculate_f(self, node):
        """计算综合f值"""
        f = 0
        for k in self.weights.keys():
            f += (node['g'][k] + node['h'][k]) * self.weights[k]
        return f
    
    def _reconstruct_path(self, node):
        """重建路径"""
        path = []
        while node:
            path.append(node['position'])
            node = node['parent']
        return path[::-1]
    
    def _euclidean_distance(self, pos1, pos2):
        """计算欧几里得距离"""
        return ((pos1[0] - pos2[0]) ** 2 + (pos1[1] - pos2[1]) ** 2) ** 0.5
    
    def _time_estimate(self, pos1, pos2):
        """估计时间"""
        # 实现时间估计逻辑
        pass
    
    def _risk_estimate(self, pos1, pos2):
        """估计风险"""
        # 实现风险估计逻辑
        pass

class DynamicHeuristic:
    def __init__(self):
        self.adjustment_factor = 1.0
    
    def adjust(self, environment):
        """根据环境调整启发函数"""
        # 分析环境因素
        obstacle_density = self._calculate_obstacle_density(environment)
        path_complexity = self._calculate_path_complexity(environment)
        
        # 调整启发函数参数
        if obstacle_density > 0.5:
            # 障碍物密集，增加启发函数权重
            self.adjustment_factor = 1.5
        elif path_complexity > 0.7:
            # 路径复杂，减少启发函数权重
            self.adjustment_factor = 0.8
        else:
            # 正常环境
            self.adjustment_factor = 1.0
    
    def calculate_heuristic(self, pos1, pos2):
        """计算调整后的启发值"""
        distance = ((pos1[0] - pos2[0]) ** 2 + (pos1[1] - pos2[1]) ** 2) ** 0.5
        return distance * self.adjustment_factor
    
    def _calculate_obstacle_density(self, environment):
        """计算障碍物密度"""
        # 实现障碍物密度计算
        pass
    
    def _calculate_path_complexity(self, environment):
        """计算路径复杂度"""
        # 实现路径复杂度计算
        pass

class ParallelAStar:
    def __init__(self, num_threads=4):
        self.num_threads = num_threads
    
    def search(self, start, goal, graph):
        """并行A*搜索"""
        # 分割搜索空间
        search_spaces = self._split_search_space(start, goal, graph)
        
        # 创建线程池
        with concurrent.futures.ThreadPoolExecutor(max_workers=self.num_threads) as executor:
            # 并行执行搜索
            futures = []
            for space in search_spaces:
                future = executor.submit(self._astar_search, start, goal, graph, space)
                futures.append(future)
            
            # 收集结果
            results = []
            for future in concurrent.futures.as_completed(futures):
                result = future.result()
                if result:
                    results.append(result)
        
        # 选择最优路径
        if results:
            return min(results, key=lambda path: len(path))
        return None
    
    def _split_search_space(self, start, goal, graph):
        """分割搜索空间"""
        # 实现搜索空间分割逻辑
        pass
    
    def _astar_search(self, start, goal, graph, space):
        """单线程A*搜索"""
        # 实现带剪枝的A*搜索
        pass
    
    def _prune(self, node, graph):
        """剪枝优化"""
        # 实现剪枝逻辑
        pass

class AStarConfig:
    def __init__(self):
        self.weights = {
            'distance': 0.3,
            'time': 0.3,
            'risk': 0.2,
            'stealth': 0.2
        }
        self.heuristic_weight = 1.0
        self.parallel_threads = 4
        self.prune_threshold = 0.5
    
    def get_config(self):
        return {
            'weights': self.weights,
            'heuristic_weight': self.heuristic_weight,
            'parallel_threads': self.parallel_threads,
            'prune_threshold': self.prune_threshold
        }