Particle Swarm Optimization for New Energy Truck Scheduling in Network Environment

doi:10.16182/j.issn1004731x.joss.22-0116

Abstract

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 improved so that the inertia weight decreases non-linearly, and the 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.

Key words: V2X(vehicle to everything), new energy truck, inertia weight, path encoding, particle swarm algorithm

CLC Number:

TP18

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.

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测试函数	PF特征	决策变量	目标空间	种群规模
ZDT1	凸，连续	30	2	200
ZDT2	凹，连续	30	2	200
ZDT3	非连续	30	2	200
ZDT4	凸，连续	30	2	200
ZDT6	凹，连续	30	2	200

测试函数	改进的		传统的
测试函数	Mean	Std.	Mean	Std.
ZDT1	1.058E-02	1.428E-03	3.593E-01	7.041E-02
ZDT2	3.444E-01	3.630E-01	4.034E-01	4.052E-01
ZDT3	2.426E-02	3.118E-02	4.897E-02	4.893E-02
ZDT4	1.357E+01	3.979E+00	1.533E+01	1.362E+01
ZDT6	2.284E+00	2.108E+00	2.489E+00	2.105E+00

测试函数	改进的		传统的
测试函数	Mean	Std.	Mean	Std.
ZDT1	1.222E-02	1.635E-03	1.281E-02		1.777E-03
ZDT2	1.741E-03	3.173E-03	1.208E-02		4.352E-03
ZDT3	4.595E-02	2.426E+00	4.852E-02		1.218E-01
ZDT4	2.507E-02	1.252E+00	2.742E-02		1.138E-02
ZDT6	1.135E-02	2.108E+00	1.469E-02		3.100E-02

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