Affiliations: [a] Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perak, Malaysia | [b] Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Shah Alam, Malaysia | [c] Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Perak, Malaysia
Correspondence:
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Corresponding author: S. Masrom, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perak, Malaysia. E-mail: [email protected]
Abstract: Particle Swarm Optimization (PSO) is a well known technique for solving various kinds of combinatorial optimization problems including scheduling, resource allocation and vehicle routing. However, basic PSO suffers from premature convergence problem. Many techniques have been proposed for alleviating this problem. One of the alternative approaches is hybridization. Genetic Algorithms (GAs) are one of the possible techniques used for hybridization. Most often, a mutation scheme is added to the PSO, but some applications of crossover have been added more recently. Some of these schemes use dynamic parameterization when applying the GA operators. In this work, dynamic parameterized mutation and crossover operators are combined with a PSO implementation individually and in combination to test the effectiveness of these additions. The results indicate that all the PSO hybrids with dynamic probability have shown satisfactory performance in finding the best distance of the Vehicle Routing Problem With Time Windows.
Keywords: Hybridization, particle swarm optimization, genetic algorithm, dynamic parameterization, vehicle routing problem with time windows
