在车辆路径问题的距离成本函数内获得当前SearchDepth

import math

def calculate_distance(point1, point2):
    """计算两点之间的欧几里得距离"""
    return math.sqrt((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2)

def distance_cost_function(route, points):
    """计算路径的总距离成本"""
    total_distance = 0
    for i in range(len(route) - 1):
        total_distance += calculate_distance(points[route[i]], points[route[i + 1]])
    return total_distance

def search_algorithm(points, max_depth):
    """模拟搜索算法，控制搜索深度"""
    best_route = None
    best_cost = float('inf')
    
    for depth in range(max_depth):
        # 这里可以插入具体的搜索逻辑，如遗传算法、模拟退火等
        current_route = generate_random_route(points)  # 示例：生成随机路径
        current_cost = distance_cost_function(current_route, points)
        
        if current_cost < best_cost:
            best_cost = current_cost
            best_route = current_route
    
    return best_route, best_cost

# 示例使用
points = [(0, 0), (1, 2), (3, 1), (5, 3)]  # 示例点集
max_depth = 100  # 设置最大搜索深度
best_route, best_cost = search_algorithm(points, max_depth)
print("Best Route:", best_route)
print("Best Cost:", best_cost)