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Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments

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  • du Plessis, Mathys C.
  • Engelbrecht, Andries P.

Abstract

This paper proposes two adaptations to DynDE, a differential evolution-based algorithm for solving dynamic optimization problems. The first adapted algorithm, Competitive Population Evaluation (CPE), is a multi-population DE algorithm aimed at locating optima faster in the dynamic environment. This adaptation is based on allowing populations to compete for function evaluations based on their performance. The second adapted algorithm, Reinitialization Midpoint Check (RMC), is aimed at improving the technique used by DynDE to maintain populations on different peaks in the search space. A combination of the CPE and RMC adaptations is investigated. The new adaptations are empirically compared to DynDE using various problem sets. The empirical results show that the adaptations constitute an improvement over DynDE and compares favorably to other approaches in the literature. The general applicability of the adaptations is illustrated by incorporating the combination of CPE and RMC into another Differential Evolution-based algorithm, jDE, which is shown to yield improved results.

Suggested Citation

  • du Plessis, Mathys C. & Engelbrecht, Andries P., 2012. "Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments," European Journal of Operational Research, Elsevier, vol. 218(1), pages 7-20.
    • Handle: RePEc:eee:ejores:v:218:y:2012:i:1:p:7-20
    DOI: 10.1016/j.ejor.2011.08.031
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    References listed on IDEAS

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    1. Omran, Mahamed G.H. & Engelbrecht, Andries P. & Salman, Ayed, 2009. "Bare bones differential evolution," European Journal of Operational Research, Elsevier, vol. 196(1), pages 128-139, July.
    2. Salman, Ayed & Engelbrecht, Andries P. & Omran, Mahamed G.H., 2007. "Empirical analysis of self-adaptive differential evolution," European Journal of Operational Research, Elsevier, vol. 183(2), pages 785-804, December.
    3. Kaelo, P. & Ali, M.M., 2006. "A numerical study of some modified differential evolution algorithms," European Journal of Operational Research, Elsevier, vol. 169(3), pages 1176-1184, March.
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    1. Wu Zhu & Jian-an Fang & Yang Tang & Wenbing Zhang & Wei Du, 2012. "Digital IIR Filters Design Using Differential Evolution Algorithm with a Controllable Probabilistic Population Size," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.

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