Electrotechnical and Computer Engineering
Vol. 39 No. 11 (2024): Proceedings of Faculty of Technical Sciences
ANALYSIS OF PARTICLE SWARM ALGORITHMS FOR THE ELECTRIC VEHICLE ROUTING PROBLEM
Abstract
This paper addresses the application of Particle Swarm Optimization (PSO) and its enhanced version, Generalized Particle Swarm Optimization (GPSO), in solving electric vehicle routing problem (EVRP). The main objective is to compare the performance of PSO and GPSO using different particle representations, specifically: priority vector and adjacency matrix. Experimental results show that the matrix representation significantly outperforms the vector representation, and the GPSO achieves better performance than the basic PSO. Additionally, the paper provides explanations of two algorithms based on the Bellman-Ford algorithm, which serve to divide the complete route into smaller ones and determine optimal positions for charging stations.
References
[1] J. Ochelska-Mierzejewska, A. Poniszewska-Marańda, and W. Marańda, "Selected genetic algorithms for vehicle routing problem solving," *Electronics*, vol. 10, no. 24, p. 3147, Dec. 2021.
[2] I. Kucukoglu, R. Dewil, and D. Cattrysse, "The electric vehicle routing problem and its variations: A literature review," *Computers & Industrial Engineering*, vol. 161, p. 107650, Nov. 2021.
[3] M. R. Rapaic, Z. Kanovic, Z. D. Jelicic, and D. Petrovacki, "Generalized PSO algorithm—An application to Lorenz system identification by means of neural-networks," in *Proc. 9th Symposium on Neural Network Applications in Electrical Engineering*, Sep. 2008, pp. 31-35.
[4] C. Prins, N. Labadi, and M. Reghioui, "Tour splitting algorithms for vehicle routing problems," *International Journal of Production Research*, vol. 47, no. 2, pp. 507-535, Jan. 2009.
[5] M. Schneider, A. Stenger, and D. Goeke, "The electric vehicle-routing problem with time windows and recharging stations," *Transportation Science*, vol. 48, no. 4, pp. 500-520, Nov. 2014.