Hybrid genetic algorithm for undirected traveling salesman problems with profits

IF 1.6 4区计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Networks Pub Date : 2023-06-21 DOI:10.1002/net.22167

P. He, Jin-Kao Hao, Qinghua Wu

引用次数: 1

Abstract

The orienteering problem (OP) and prize‐collecting traveling salesman problem (PCTSP) are two typical TSPs with profits, in which each vertex has a profit and the goal is to visit several vertices to optimize the collected profit and travel costs. The OP aims to collect the maximum profit without exceeding the given travel cost. The PCTSP seeks to minimize the travel costs while ensuring a minimum profit threshold. This study introduces a hybrid genetic algorithm that addresses both the OP and PCTSP under a unified framework. The algorithm combines an extended edge‐assembly crossover operator to produce promising offspring solutions, and an effective local search to ameliorate each offspring solution. The algorithm is further enforced by diversification‐oriented mutation and population‐diversity management. Extensive experiments demonstrate that the method competes favorably with the best existing methods in terms of both the solution quality and computational efficiency. Additional experiments provide insights into the roles of the key components of the proposed method.

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求解有利润无向旅行商问题的混合遗传算法

定向越野问题（OP）和奖品收集旅行推销员问题（PCTSP）是两个典型的有利润的TSP，其中每个顶点都有利润，目标是访问几个顶点以优化收集的利润和旅行成本。OP的目标是在不超过给定差旅成本的情况下获得最大利润。PCTSP旨在最大限度地降低差旅成本，同时确保最低利润阈值。本研究介绍了一种混合遗传算法，该算法在统一的框架下同时解决OP和PCTSP问题。该算法结合了一个扩展的边缘组装交叉算子来产生有前景的子代解决方案，以及一个有效的局部搜索来改进每一个子代解决方案。该算法通过面向多样化的突变和种群多样性管理得到进一步加强。大量实验表明，该方法在求解质量和计算效率方面都与现有的最佳方法相竞争。额外的实验提供了对所提出的方法的关键组成部分的作用的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Networks 工程技术-计算机：硬件

CiteScore

4.40

自引率

9.50%

发文量

审稿时长

12 months

期刊介绍： Network problems are pervasive in our modern technological society, as witnessed by our reliance on physical networks that provide power, communication, and transportation. As well, a number of processes can be modeled using logical networks, as in the scheduling of interdependent tasks, the dating of archaeological artifacts, or the compilation of subroutines comprising a large computer program. Networks provide a common framework for posing and studying problems that often have wider applicability than their originating context. The goal of this journal is to provide a central forum for the distribution of timely information about network problems, their design and mathematical analysis, as well as efficient algorithms for carrying out optimization on networks. The nonstandard modeling of diverse processes using networks and network concepts is also of interest. Consequently, the disciplines that are useful in studying networks are varied, including applied mathematics, operations research, computer science, discrete mathematics, and economics. Networks publishes material on the analytic modeling of problems using networks, the mathematical analysis of network problems, the design of computationally eﬃcient network algorithms, and innovative case studies of successful network applications. We do not typically publish works that fall in the realm of pure graph theory (without signiﬁcant algorithmic and modeling contributions) or papers that deal with engineering aspects of network design. Since the audience for this journal is then necessarily broad, articles that impact multiple application areas or that creatively use new or existing methodologies are especially appropriate. We seek to publish original, well-written research papers that make a substantive contribution to the knowledge base. In addition, tutorial and survey articles are welcomed. All manuscripts are carefully refereed.