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Micro-MOPSO: A Multi-Objective Particle Swarm Optimizer That Uses a Very Small Population Size

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Multi-Objective Swarm Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 261))

Abstract

In this chapter, we present a multi-objective evolutionary algorithm (MOEA) based on the heuristic called “particle swarm optimization” (PSO). This multi-objective particle swarm optimizer (MOPSO) is characterized for using a very small population size, which allows it to require a very low number of objective function evaluations (only 3000 per run) to produce reasonably good approximations of the Pareto front of problems of moderate dimensionality. The proposed approach first selects the leader and then selects the neighborhood for integrating the swarm. The leader selection scheme adopted is based on Pareto dominance and uses a neighbors density estimator. Additionally, the proposed approach performs a reinitialization process for preserving diversity and uses two external archives: one for storing the solutions that the algorithm finds during the search process and another for storing the final solutions obtained. Furthermore, a mutation operator is incorporated to improve the exploratory capabilities of the algorithm. The proposed approach is validated using standard test functions and performance measures reported in the specialized literature. Our results are compared with respect to those generated by the Nondominated Sorting Genetic Algorithm II (NSGA-II), which is a MOEA representative of the state-of-the-art in the area.

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Authors and Affiliations

  1. (Evolutionary Computation Group), Computer Science Department, CINVESTAV-IPN, México

    Juan Carlos Fuentes Cabrera & Carlos A. Coello Coello

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  1. Juan Carlos Fuentes Cabrera
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  2. Carlos A. Coello Coello
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Editors and Affiliations

  1. Faculdade de Engenharia, Universidade do Estado do Rio de Janeiro, sala 5022-D Rua São Francisco Xavier 524, 20550-900, MARACANÃ, RJ, Brazil

    Nadia Nedjah  & Luiza de Macedo Mourelle  & 

  2. Universidade Federal do Paraná Departamento de Engenharia Elétrica Pós-Graduação em Engenharia Elétrica Centro Politécnico 81531-980, Curitiba-PR, Brazil and Pontifícia Universidade Católica do Paraná Centro, Brazil

    Leandro dos Santos Coelho

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Cabrera, J.C.F., Coello, C.A.C. (2010). Micro-MOPSO: A Multi-Objective Particle Swarm Optimizer That Uses a Very Small Population Size. In: Nedjah, N., dos Santos Coelho, L., de Macedo Mourelle, L. (eds) Multi-Objective Swarm Intelligent Systems. Studies in Computational Intelligence, vol 261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05165-4_4

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  • DOI: https://doi.org/10.1007/978-3-642-05165-4_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05164-7

  • Online ISBN: 978-3-642-05165-4

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