This paper investigates the ability of a discrete particle swarm optimization algorithm (DPSO) to evolve solutions from infeasibility to feasibility for the vehicle routing problem with time windows (VRPTW). The proposed algorithm incorporates some principles from multi-objective optimization to allow particles to conduct a dynamic trade-off between objectives in order to reach feasibility. The main contribution of this paper is to demonstrate that without incorporating tailored heuristics or operators to tackle infeasibility, it is possible to evolve very poor infeasible route-plans to very good feasible ones using swarm intelligence. Section 2 describes the particle swarm optimization (PSO) paradigm which is used in this paper to tackle the VRPTW. Section 3 describes the multi-objective discrete PSO proposed in this paper to evolve infeasible solutions to feasible ones using a dynamic trade-off of the multiple objectives. Section 4 describes and discusses experiments and results while Section 5 concludes this paper.
Exploring feasible and infeasible regions in the vehicle routing problem with time windows using a multi-objective particle swarm optimization approach
Untersuchung zulässiger und nichtzulässiger Bereiche beim Tourenplanungsproblem mit Zeitfenstern mit Hilfe eines multikriteriellen Partikelschwarm-Optimierungsansatzes
2009
12 Seiten, 2 Bilder, 1 Tabelle, 25 Quellen
Conference paper
English
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