The classic problem of \textit{constrained path finding} is a well-studied but yet challenging topic in AI with a broad range of applications in various areas such communication and transportation. The Weight Constrained Shortest Path Problem (WCSPP), as the base form of constrained path finding with only one side constraint, aims to plan a cost optimum path whose weight/resource usage is limited. Given the bi-criteria nature of the problem (i.e., dealing with cost and weight of paths), methods addressing the WCSPP have some common properties with bi-objective search. This paper leverages the recent state-of-the-art A*-based techniques in both constrained path finding and bi-objective search and presents two exact solution approaches to the WCSPP, both capable of solving hard problem instances on very large graphs. We empirically evaluate the performance of our algorithms on a new set of large and realistic problem instances and show their advantages over the state-of-the-art algorithms in both time and space metrics. This paper also investigates the importance of priority queues in constrained search with A*. We show with extensive experiments on both realistic and randomised graphs how bucket-based queues without tie-breaking can effectively improve the algorithmic performance of exhaustive bi-criteria searches.
翻译:经典的“Textit”{受限制的路径发现”问题是一个研究周全但具有挑战性的议题,AI在各个领域应用了广泛的通信和运输等。 体重限制最短路径问题(WCSPP)作为有限路径寻找的基础形式,其基本形式只有一个侧面制约,目的是规划一种成本最佳路径,其重量/资源使用有限。鉴于这一问题的双标准性质(即处理路径的成本和重量),处理WCSP的方法具有一些共同的特性,有双目标搜索。本文还利用最近的最先进的A* 技术,利用了最近最先进的A* 技术,在有限的路径发现和双目标搜索方面,向WCSPPP提供两种确切的解决办法,两者都能够解决非常大的图表上的难题。我们用经验评估了我们对于一系列新的大型和现实问题实例的算法的性表现,并展示了它们在时间和空间测量中最先进的算法的优势。本文还用A* 调查了在限制搜索中以最先进的A* 和双目标搜索中以最先进的技术的重要性。我们用大量的方法在不现实和最彻底的图表上都展示了一次彻底的模型。