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Particle Swarm Optimization Algorithm with Multi-strategies for Delay Scheduling

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Abstract

In order to improve the convergence speed and solution accuracy of particle swarm optimization (PSO) algorithm and avoid premature convergence, an enhanced PSO with fusing multiple strategies, namely CWBPSO is proposed in this paper. In the proposed CWBPSO algorithm, a fast convergence strategy is employed to accelerate the particles toward the optimal value. Meanwhile, an improved strategy of the acceleration factor is designed to improve the local search ability of the particles and strengthen the global search ability. A new linear decreasing strategy of inertia weight factor is designed to avoid premature maturation and oscillation phenomenon, improve the overall optimization performance and reduce the time complexity. Four typical test functions in CEC2014 and CEC2017 and a real train delay scheduling problem are selected to verify the effectiveness of the proposed CWBPSO algorithm. The comparative analysis of experimental results shows that the CWBPSO algorithm improves the convergence speed and convergence accuracy, avoids premature convergence and oscillation phenomena. The CWBPSO algorithm can effectively schedule the delay trains, reduce train delay time and avoid delay propagation.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant U2133205, U2033214 and 61771087, the Research and Innovation Funding Project for Postgraduates of Tianjin (Aviation Project) under Grant 2021YJSO2S12, the China National Key R&D Program under Grant 2018YFB1601200, the Research Foundation for Civil Aviation University of China under Grant 3122022PT02 and 2020KYQD123, and the Central University Basic Scientific Research Business Fee Project of Civil Aviation University of China under Grant 2000420534.

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Authors and Affiliations

  1. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, 300300, China

    Lirong Zhang, Junjie Xu, Huimin Zhao & Wu Deng

  2. Research Center of Big Data and Information Management, Civil Aviation Management Institute of China, Beijing, 100102, China

    Yi Liu

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  1. Lirong Zhang
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  2. Junjie Xu
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Correspondence to Wu Deng.

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Zhang, L., Xu, J., Liu, Y. et al. Particle Swarm Optimization Algorithm with Multi-strategies for Delay Scheduling. Neural Process Lett 54, 4563–4592 (2022). https://doi.org/10.1007/s11063-022-10821-w

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