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An Improved Hybrid Particle Swarm Optimization for Travel Salesman Problem

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Artificial Intelligence Algorithms and Applications (ISICA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1205))

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Abstract

Travel salesman problem (TSP) is a typical NP-complete problem, which is very hard to be optimized by traditional methods. In this paper, an improved hybrid particle swarm optimization (IHPSO) is proposed to solve the travel salesman problem (TSP). In IHPSO, there are four novel strategies proposed for improving its comprehensive performance. Firstly, a probability initialization is used to add prior knowledge into the initialization, so that much computing resources can save during the evolution of the algorithm. Furthermore, in order to improve the algorithm’s convergence accuracy and population diversity, two kinds of crossover are proposed to make better use of Gbest and Pbest. Lastly, a directional mutation is applied to overcome the randomness of the traditional mutation operator. Comparison results among IHPSO and other EAs, including PSO, genetic algorithm (GA), Tabu Search (TB), and simulated annealing algorithm (SA) on the standard TSPLIB format manifest that IHPSO is more superior to other methods, especially on large scale TSP.

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Acknowledgement

This study was funded by the National Natural Science Foundation of China (Grant Nos.: 61806204, 61663009, 61602174), the National Natural Science Foundation of Jiangxi Province (Grant Nos.: 20171BAB202019, 20161BAB202064, 20161BAB212052, 20151BAB207022), the National Natural Science Foundation of Jiangxi Provincial Department of Education (Grant Nos.: GJJ160469), and the Research Project of Jiangxi Provincial Department of Communication and Transportation (No. 2017D0038).

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Authors and Affiliations

  1. School of Software, East China Jiaotong University, Nanchang, 330013, China

    Bo Wei & Ying Xing

  2. College of Physics and Information Engineering, Minnan Normal University, Zhangzhou, 363000, China

    Xuewen Xia & Ling Gui

Authors
  1. Bo Wei
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  2. Ying Xing
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  3. Xuewen Xia
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  4. Ling Gui
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Corresponding author

Correspondence to Xuewen Xia .

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Editors and Affiliations

  1. South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, China

    Wei Li

  3. South China Agricultural University, Guangzhou, China

    Hui Wang

  4. The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Wei, B., Xing, Y., Xia, X., Gui, L. (2020). An Improved Hybrid Particle Swarm Optimization for Travel Salesman Problem. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_40

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  • DOI: https://doi.org/10.1007/978-981-15-5577-0_40

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5576-3

  • Online ISBN: 978-981-15-5577-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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