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Neural Processing Letters

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05-05-2022

Particle Swarm Optimization Algorithm with Multi-strategies for Delay Scheduling

Authors: Lirong Zhang, Junjie Xu, Yi Liu, Huimin Zhao, Wu Deng

Published in: Neural Processing Letters | Issue 5/2022

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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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Title: Particle Swarm Optimization Algorithm with Multi-strategies for Delay Scheduling
Authors: Lirong Zhang
Junjie Xu
Yi Liu
Huimin Zhao
Wu Deng
Publication date: 05-05-2022
Publisher: Springer US
Published in: Neural Processing Letters / Issue 5/2022
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-022-10821-w

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