Particle Swarm Optimization for New Energy Truck Scheduling in Network Environment

Authors

Chuanchao Zhao, School of Physics and Electronic Information, Anhui Normal University, Wuhu 241002, China; Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot (Anhui Normal University), Wuhu 241002, China
Rui Zheng, School of Physics and Electronic Information, Anhui Normal University, Wuhu 241002, China; Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot (Anhui Normal University), Wuhu 241002, China
Li Gong, School of Physics and Electronic Information, Anhui Normal University, Wuhu 241002, China; Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot (Anhui Normal University), Wuhu 241002, China
Xiaolu Ma, School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243000, China

Abstract

In V2X intelligent network environment, the dispatching system of new energy trucks needs real-time dynamic information.The system under traditional particle swarm scheduling method is prone to fall into local optimum and low solution efficienty.An improved particle swarm scheduling method for new energy trucks is proposed on the basis of multi-objective optiminaztion research. The inertia weight update method is improvedso that the inertia weight decreases non-linearly, andthe risk of the system falling into local optimum is reduced.A priori path encoding method is designed and optimized,the solution efficiency of the algorithm is improved, and the energy consumption of new energy trucks is reduced. The simulation results show that the total path length, path smoothness, and algorithm convergence speed are improved. In both static and dynamic environments, the reasonable scheduling of new energy trucks is achieved by the improved method.

Recommended Citation

Zhao, Chuanchao; Zheng, Rui; Gong, Li; and Ma, Xiaolu (2023) "Particle Swarm Optimization for New Energy Truck Scheduling in Network Environment," Journal of System Simulation: Vol. 35: Iss. 6, Article 17.
DOI: 10.16182/j.issn1004731x.joss.22-0116
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss6/17

First Page

1337

Revised Date

2022-03-04

DOI Link

http://dx.doi.org/10.16182/j.issn1004731x.joss.22-0116

Last Page

1350

CLC

TP18

Recommended Citation

Chuanchao Zhao, Rui Zheng, Li Gong, Xiaolu Ma. Particle Swarm Optimization for New Energy Truck Scheduling in Network Environment[J]. Journal of System Simulation, 2023, 35(6): 1337-1350.

DOI

10.16182/j.issn1004731x.joss.22-0116