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On the Efficacy of Ensemble of Constraint Handling Techniques in Self-Adaptive Differential Evolution

Author

Listed:
  • Hassan Javed

    (Institute of Numerical Sciences, Kohat University of Science & Technology, Kohat 26000, Pakistan
    These authors contributed equally to this work.)

  • Muhammad Asif Jan

    (Institute of Numerical Sciences, Kohat University of Science & Technology, Kohat 26000, Pakistan
    These authors contributed equally to this work.)

  • Nasser Tairan

    (College of Computer Science, King Khalid University, Abha 61321, Saudi Arabia
    These authors contributed equally to this work.)

  • Wali Khan Mashwani

    (Institute of Numerical Sciences, Kohat University of Science & Technology, Kohat 26000, Pakistan
    These authors contributed equally to this work.)

  • Rashida Adeeb Khanum

    (Jinnah College for Women, University of Peshawar, Peshawar 25000, Pakistan
    These authors contributed equally to this work.)

  • Muhammad Sulaiman

    (Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistan
    These authors contributed equally to this work.)

  • Hidayat Ullah Khan

    (Department of Economics, Abbottabad University of Science & Technology, Abbottabad 22010, Pakistan
    These authors contributed equally to this work.)

  • Habib Shah

    (College of Computer Science, King Khalid University, Abha 61321, Saudi Arabia
    These authors contributed equally to this work.)

Abstract

Self-adaptive variants of evolutionary algorithms (EAs) tune their parameters on the go by learning from the search history. Adaptive differential evolution with optional external archive (JADE) and self-adaptive differential evolution (SaDE) are two well-known self-adaptive versions of differential evolution (DE). They are both unconstrained search and optimization algorithms. However, if some constraint handling techniques (CHTs) are incorporated in their frameworks, then they can be used to solve constrained optimization problems (COPs). In an early work, an ensemble of constraint handling techniques (ECHT) is probabilistically hybridized with the basic version of DE. The ECHT consists of four different CHTs: superiority of feasible solutions, self-adaptive penalty, ε -constraint handling technique and stochastic ranking. This paper employs ECHT in the selection schemes, where offspring competes with their parents for survival to the next generation, of JADE and SaDE. As a result, JADE-ECHT and SaDE-ECHT are developed, which are the constrained variants of JADE and SaDE. Both algorithms are tested on 24 COPs and the experimental results are collected and compared according to algorithms’ evaluation criteria of CEC’06. Their comparison, in terms of feasibility rate (FR) and success rate (SR), shows that SaDE-ECHT surpasses JADE-ECHT in terms of FR, while JADE-ECHT outperforms SaDE-ECHT in terms of SR.

Suggested Citation

  • Hassan Javed & Muhammad Asif Jan & Nasser Tairan & Wali Khan Mashwani & Rashida Adeeb Khanum & Muhammad Sulaiman & Hidayat Ullah Khan & Habib Shah, 2019. "On the Efficacy of Ensemble of Constraint Handling Techniques in Self-Adaptive Differential Evolution," Mathematics, MDPI, vol. 7(7), pages 1-19, July.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:7:p:635-:d:249290
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    References listed on IDEAS

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    1. David W. Coit & Alice E. Smith & David M. Tate, 1996. "Adaptive Penalty Methods for Genetic Optimization of Constrained Combinatorial Problems," INFORMS Journal on Computing, INFORMS, vol. 8(2), pages 173-182, May.
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