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Differential evolution with multi-constraint consensus methods for constrained optimization

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  • Noha Hamza
  • Ruhul Sarker
  • Daryl Essam

Abstract

Constrained optimization is an important research topic that assists in quality planning and decision making. To solve such problems, one of the important aspects is to improve upon any constraint violation, and thus bring infeasible individuals to the feasible region. To achieve this goal, different constraint consensus methods have been introduced, but no single method performs well for all types of problems. Hence, in this research, for solving constrained optimization problems, we introduce different variants of the Differential Evolution algorithm, with multiple constraint consensus methods. The proposed algorithms are tested and analyzed by solving a set of well-known bench mark problems. For further improvements, a local search is applied to the best variant. We have compared our algorithms among themselves, as well as with other state of the art algorithms. Those comparisons show similar, if not better performance, while also using significantly lower computational time. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Noha Hamza & Ruhul Sarker & Daryl Essam, 2013. "Differential evolution with multi-constraint consensus methods for constrained optimization," Journal of Global Optimization, Springer, vol. 57(2), pages 583-611, October.
    • Handle: RePEc:spr:jglopt:v:57:y:2013:i:2:p:583-611
    DOI: 10.1007/s10898-012-9987-z
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    References listed on IDEAS

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    1. Y. Xiao & D. Michalski & J.M. Galvin & Y. Censor, 2003. "The Least-Intensity Feasible Solution for Aperture-Based Inverse Planning in Radiation Therapy," Annals of Operations Research, Springer, vol. 119(1), pages 183-203, March.
    2. John W. Chinneck, 2004. "The Constraint Consensus Method for Finding Approximately Feasible Points in Nonlinear Programs," INFORMS Journal on Computing, INFORMS, vol. 16(3), pages 255-265, August.
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    Cited by:

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