Implementation of an H-PSOGA Optimization Model for Vehicle Routing Problem

Justice Kojo Kangah, Justice Kwame Appati, Kwaku F. Darkwah and Michael Agbo Tettey Soli
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Justice Kojo Kangah: Kwame Nkrumah University of Science and Technology, Ghana
Justice Kwame Appati: University of Ghana, Ghana
Kwaku F. Darkwah: Kwame Nkrumah University of Science and Technology, Ghana
Michael Agbo Tettey Soli: University of Ghana, Ghana

International Journal of Applied Metaheuristic Computing (IJAMC), 2021, vol. 12, issue 3, 148-162

Abstract: This work presents an ensemble method which combines both the strengths and weakness of particle swarm optimization (PSO) with genetic algorithm (GA) operators like crossover and mutation to solve the vehicle routing problem. Given that particle swarm optimization and genetic algorithm are both population-based heuristic search evolutionary methods as used in many fields, the standard particle swarm optimization stagnates particles more quickly and converges prematurely to suboptimal solutions which are not guaranteed to be local optimum. Although both PSO and GA are approximation methods to an optimization problem, these algorithms have their limitations and benefits. In this study, modifications are made to the original algorithmic structure of PSO by updating it with some selected GA operators to implement a hybrid algorithm. A computational comparison and analysis of the results from the non-hybrid algorithm and the proposed hybrid algorithm on a MATLAB simulation environment tool show that the hybrid algorithm performs quite well as opposed to using only GA or PSO.

Date: 2021
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