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Applications of particle swarm optimization in the railway domain

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  • Qing Wu
  • Colin Cole
  • Tim McSweeney

Abstract

This paper provides a comprehensive review regarding the applications of particle swarm optimization (PSO) in the railway domain. One hundred and thirty nine (139) publications in the railway domain are listed and summarized. The review indicates that PSO has seen more and more applications in the railway domain in recent years; scheduling, active controls, and network layout planning represent the three largest application areas. PSO variants such as genetic PSO, chaotic PSO, and quantum-behaved PSO are also used in the railway domain. The inertial weight has been widely accepted and used in railway applications, while the contraction coefficient and variable velocity limit have seen fewer applications. Optimization of vehicle mechanical systems dynamics has been identified as an area that has the potential for more applications. From this paper, researchers from other areas of the railway domain can identify many other potential applications. Parallel PSO was not found in previous railway applications; it can be one direction to leverage the PSO applications by improving the computational speed. Two new application cases of parallel PSO for railway vehicle designs were presented.

Suggested Citation

  • Qing Wu & Colin Cole & Tim McSweeney, 2016. "Applications of particle swarm optimization in the railway domain," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 4(3), pages 167-190, August.
    • Handle: RePEc:taf:tjrtxx:v:4:y:2016:i:3:p:167-190
    DOI: 10.1080/23248378.2016.1179599
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    Cited by:

    1. Khattak, Afaq & Hussain, Arshad, 2021. "Hybrid DES-PSO framework for the design of commuters’ circulation space at multimodal transport interchange," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 180(C), pages 205-229.
    2. Jiang Liu & Tian-tian Li & Bai-gen Cai & Jiao Zhang, 2020. "Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line," Energies, MDPI, vol. 13(8), pages 1-25, April.
    3. Barbara Kulesz & Andrzej Sikora & Adam Zielonka, 2018. "Application Of Ant-Colony Algorithm To The Issue Of Improving Rectified Voltage Parameters In Electric Tram Traction," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 13(2), pages 133-144, June.
    4. Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2018. "Optimizing headways for urban rail transit services using adaptive particle swarm algorithms," Public Transport, Springer, vol. 10(1), pages 23-62, May.
    5. repec:exl:1trans:v:13:y:2018:i:2:p:- is not listed on IDEAS
    6. Aredah, Ahmed & Fadhloun, Karim & Rakha, Hesham A., 2024. "Energy optimization in freight train operations: Algorithmic development and testing," Applied Energy, Elsevier, vol. 364(C).

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