Abstract
The PSO (Particle Swarm Optimization) metaheuristics, originally defined for solving single-objective problems, has been applied to multi-objective problems with very good results. In its initial conception, the algorithm has a fixed-size population. In this paper, a new variation of this metaheuristics, called VarMOPSO (Variable Multi-Objective Particle Swarm Optimization), characterized by a variable-sized population, is proposed. To this end, the concepts of age and neighborhood are incorporated to be able to modify the size of the population for the different generations. This algorithm was compared with the version that uses fixed-size populations, as well as with other metaheuristics, all of them representative of the state of the art in multi-objective optimization. In all cases, three widely used metrics were considered as quality indicators for Pareto front. The results obtained were satisfactory.
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López, J., Lanzarini, L., De Giusti, A. (2010). VarMOPSO: Multi-Objective Particle Swarm Optimization with Variable Population Size. In: Kuri-Morales, A., Simari, G.R. (eds) Advances in Artificial Intelligence – IBERAMIA 2010. IBERAMIA 2010. Lecture Notes in Computer Science(), vol 6433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16952-6_7
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DOI: https://doi.org/10.1007/978-3-642-16952-6_7
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