Research Output

PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation

  This paper deals with the study of the cooperation between parallel processing and evolutionary computation to obtain efficient procedures for solving multiobjective optimisation problems. We propose a new algorithm called PSFGA (parallel single front genetic algorithm), an elitist evolutionary algorithm for multiobjective problems with a clearing procedure that uses a grid in the objective space for diversity maintaining purposes. Thus, PSFGA is a parallel genetic algorithm with a structured population in the form of a set of islands. The performance analysis of PSFGA has been carried out in a cluster system and experimental results show that our parallel algorithm provides adequate results in both, the quality of the solutions found and the time to obtain them. It has been shown that its sequential version also outperforms other previously proposed sequential procedures for multiobjective optimisation in the cases studied

  • Type:

    Article

  • Date:

    30 November 2003

  • Publication Status:

    Published

  • Publisher

    Elsevier Science

  • DOI:

    10.1016/j.parco.2003.12.012

  • ISSN:

    0167-8191

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

Citation

de Toro Negro, F., Ortega, J., Ros, E., Mota, S., Paechter, B. & Martin, J. M. (2003). PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation. Parallel Computing. 30, 551-816. doi:10.1016/j.parco.2003.12.012. ISSN 0167-8191

Authors

Keywords

cluster of computers; multiobjective optimisation; parallel evolutionary algorithms

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