用人工智能搜索求解广义覆盖TSP的启发式函数

M. Greco, Carlos Hernández
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引用次数: 0

摘要

搜索是人工智能中通用的解决问题的方法。具体来说,启发式搜索算法,如A*,使用启发式函数来指导搜索过程。启发式函数允许算法只探索搜索空间的一部分,从而产生高效的搜索过程。提出了一种求解广义覆盖旅行商问题的新启发式函数。启发式函数是预先计算的。该函数的获取方法是对GCTSP中难度不断增加的小子问题,使用增量式最优优先搜索算法,重复使用预先计算的启发式值,连续预计算最优解。所得到的启发式函数可用于不同的启发式搜索算法。据我们所知,启发式搜索还没有解决这个问题。本文首次使用启发式搜索算法,如a *和任意时间搜索算法,来解决GCTSP问题。我们评估了不同的启发式搜索算法。实验评估表明,结果质量相同,比传统运筹学中使用的精确方法快了几个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Heuristic Function to Solve The Generalized Covering TSP with Artificial Intelligence Search
Search is a universal problem-solving method in Artificial Intelligence. Specifically, Heuristic Search algorithms, such as A*, use a heuristic function to guide the search process. The heuristic function allows algorithms to explore only a part of the search space, resulting in an efficient search process. This paper presents a new heuristic function to solve the Generalized Covering Traveling Salesman Problem (GCTSP). The heuristic function is precalculated. The method to obtain the function is pre-calculating optimal solutions consecutively to small sub-problems of the GCTSP of increasing difficulty, using an incremental Best First Search algorithm, which reuses heuristics values previously precalculated. The resultating heuristic function can be used with different heuristic search algorithms. To the best of our knowledge, this problem has not been solved with Heuristic Search. This paper is the first to present a solution to the GCTSP using Heuristic Search algorithms, such as A* and Anytime search algorithms. We evaluated different Heuristic Search algorithms. The experimental evaluation shows results of the same quality, obtained orders-of-magnitude faster than the exact methods traditionally used in Operations Research.
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