An attractors-based particle swarm optimization for multiobjective capacitated vehicle routing problem

In this paper, a new multiobjective discrete particle swarm algorithm is presented for the Capacitated vehicle routing problem. The binary algorithm integrates particle displacement based on local attractors, a crowding distance as elitism policy and genetic operators. The proposed approach is first implemented on a set of well-known benchmarks for single-objective capacitated vehicle routing problems and compared to results underlined in the literature. The obtained results demonstrate its ability to achieve the main optimization solution and sometimes prove its efficiency from other given techniques. Then, the approach is applied to an academical example for the multiobjective Urban Bus Routing Problem with Route Balancing.