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PSO-DE-Based Regional Scheduling Method for Shared Vehicles

PSO-DE-Based Regional Scheduling Method for Shared Vehicles Numerous parking spots are generated during the use of shared cars. However, in the scheduling process, if only the condition of satisfying the number of parking points is considered, it will bring substantial scheduling cost. An attempt is made in this paper to address this situation by first using the K-means algorithm to zone the parking points. Then, a zonal scheduling model based on the theory of differential evolution (DE) is developed, and a reasonable reward and penalty mechanism is set for vehicle parking based on historical usage. Finally, a hybrid particle swarm optimization-differential evolution (PSO-DE) optimization algorithm is used to improve the optimization search process of the model. The experimental results show that the optimization effect of using the PSO-DE algorithm has significantly improved as compared to the DE algorithm alone. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

PSO-DE-Based Regional Scheduling Method for Shared Vehicles

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References (24)

Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2023. ISSN 0146-4116, Automatic Control and Computer Sciences, 2023, Vol. 57, No. 2, pp. 167–176. © Allerton Press, Inc., 2023.
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/s0146411623020116
Publisher site
See Article on Publisher Site

Abstract

Numerous parking spots are generated during the use of shared cars. However, in the scheduling process, if only the condition of satisfying the number of parking points is considered, it will bring substantial scheduling cost. An attempt is made in this paper to address this situation by first using the K-means algorithm to zone the parking points. Then, a zonal scheduling model based on the theory of differential evolution (DE) is developed, and a reasonable reward and penalty mechanism is set for vehicle parking based on historical usage. Finally, a hybrid particle swarm optimization-differential evolution (PSO-DE) optimization algorithm is used to improve the optimization search process of the model. The experimental results show that the optimization effect of using the PSO-DE algorithm has significantly improved as compared to the DE algorithm alone.

Journal

Automatic Control and Computer Sciences – Springer Journals

Published: Apr 1, 2023

Keywords: K-means; differential evolution; particle swarm optimization; PSO-DE; car scheduling

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