基于Clarke - Wright和局部搜索的旅行商问题复合算法

Komarudin, Sandiego F. Parhusip
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引用次数: 1

摘要

旅行商问题(TSP)是一个可以用启发式复合算法求解的NP-hard优化问题。复合算法是一种启发式优化模型,它结合了线路构建算法和线路改进算法。Clarke Wright节约启发式算法是产生良好初始解的最佳方法之一,而局部搜索算法是获得改进解的成功方法。本文将提出一种基于Clarke wright savings和局部搜索K-opt的复合算法作为求解TSP的初步模型。实验结果表明,所提出的算法可以求解旅行推销员问题(Traveling Salesman problem)的一个大问题实例,解出85.900个点,结果具有竞争性,30个问题实例的计算时间变化较小,计算时间相对较短。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Composite Algorithm Based on Clarke - Wright and Local Search for the Traveling Salesman Problem
Traveling Salesman Problem (TSP) is an NP-hard optimization problem that can be solved by a heuristic composite algorithm. A composite algorithm is a heuristic optimization model that combine tour construction algorithm and tour improvement algorithm. Clarke Wright Savings heuristic is one of the best methods that produce a good initial solution, and local search is known to be a successful operator to make an improvement solution. This paper will present a composite algorithm as a preliminary model based on Clarke wright savings and local search K-opt to solve TSP. The experimental result shows that the proposed algorithm can solve a large problem instance of Traveling Salesman Problem up to 85.900 points, with competitive results, small variations of computing time for 30 problem instances, and relatively short computing time.
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