A new self-adaptive approach for evolutionary multiobjective optimization

Lucas S. Batista, Felipe Campelo, Frederico G. Guimaraes, Jaime A. Ramirez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose in this paper a new strategy for self-adaptation in multiobjective evolutionary algorithms, which is based on information obtained from the implicit distribution created by a chaotic differential mutation operator. This technique is used to develop a self-adaptive evolutionary algorithm for multiobjective optimisation, and its efficiency is evaluated by means of a comparative study using well-known benchmark problems. The statistical analysis of the results shows that the proposed algorithm was able to outperform the NSGA-II in fourteen of the seventeen problems used. These results represent evidence for the adequacy of the proposed technique in solving the classes of multiobjective optimisation problems represented in the benchmark suites used.
Original languageEnglish
Title of host publicationProceedings of the IEEE Congress on Evolutionary Computation, CEC 2010
PublisherIEEE
Number of pages8
ISBN (Print)9781424469093
DOIs
Publication statusPublished - 27 Sep 2010

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  • Activities

    • 1 Participation in conference

    IEEE Congress on Evolutionary Computation

    Felipe Campelo Franca Pinto (Participant)

    2010

    Activity: Participating in or organising an event typesParticipation in conference

    Cite this

    Batista, L. S., Campelo, F., Guimaraes, F. G., & Ramirez, J. A. (2010). A new self-adaptive approach for evolutionary multiobjective optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010 [7641] IEEE. https://doi.org/10.1109/cec.2010.5586512