解决广义旅行推销员问题的新型记忆算法

Pub Date : 2024-03-27 DOI:10.1093/jigpal/jzae019
Ovidiu Cosma, Petrică C Pop, Laura Cosma
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引用次数: 0

摘要

本文研究了广义旅行推销员问题(GTSP),它是著名的旅行推销员问题(TSP)的扩展,它在一个聚类图中寻找最佳巡回路线,使得每个聚类图都被访问一次。在本文中,我们介绍了一种高效解决 GTSP 的新型记忆算法 (MA)。我们提出的记忆算法以遗传算法(GA)为核心,由基于 TSP 求解器和最短路径(SP)算法的染色体增强程序(CEP)完成,为改善 GA 的收敛特性,对每一代中的最佳染色体采用了局部搜索(LS)操作。我们在文献中的一组著名实例上测试了我们的算法,结果证明我们的新型记忆算法与专业文献中的现有求解方法相比具有很强的竞争力。
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A novel memetic algorithm for solving the generalized traveling salesman problem
This paper investigates the Generalized Traveling Salesman Problem (GTSP), which is an extension of the well-known Traveling Salesman Problem (TSP), and it searches for an optimal tour in a clustered graph, such that every cluster is visited exactly once. In this paper, we describe a novel Memetic Algorithm (MA) for solving efficiently the GTSP. Our proposed MA has at its core a genetic algorithm (GA), completed by a Chromosome Enhancement Procedure (CEP), which is based on a TSP solver and the Shortest Path (SP) algorithm and for improving the convergence characteristics of the GA, a Local Search (LS) operation is applied for the best chromosomes in each generation. We tested our algorithm on a set of well-known instances from the literature and the achieved results prove that our novel memetic algorithm is highly competitive against the existing solution approaches from the specialized literature.
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