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Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization

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  • Nikhil Padhye
  • Pulkit Mittal
  • Kalyanmoy Deb

Abstract

Evolutionary algorithms (EAs) are being routinely applied for a variety of optimization tasks, and real-parameter optimization in the presence of constraints is one such important area. During constrained optimization EAs often create solutions that fall outside the feasible region; hence a viable constraint-handling strategy is needed. This paper focuses on the class of constraint-handling strategies that repair infeasible solutions by bringing them back into the search space and explicitly preserve feasibility of the solutions. Several existing constraint-handling strategies are studied, and two new single parameter constraint-handling methodologies based on parent-centric and inverse parabolic probability (IP) distribution are proposed. The existing and newly proposed constraint-handling methods are first studied with PSO, DE, GAs, and simulation results on four scalable test-problems under different location settings of the optimum are presented. The newly proposed constraint-handling methods exhibit robustness in terms of performance and also succeed on search spaces comprising up-to $$500$$ 500 variables while locating the optimum within an error of $$10^{-10}$$ 10 - 10 . The working principle of the IP based methods is also demonstrated on (i) some generic constrained optimization problems, and (ii) a classic ‘Weld’ problem from structural design and mechanics. The successful performance of the proposed methods clearly exhibits their efficacy as a generic constrained-handling strategy for a wide range of applications. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Nikhil Padhye & Pulkit Mittal & Kalyanmoy Deb, 2015. "Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization," Computational Optimization and Applications, Springer, vol. 62(3), pages 851-890, December.
    • Handle: RePEc:spr:coopap:v:62:y:2015:i:3:p:851-890
    DOI: 10.1007/s10589-015-9752-6
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    References listed on IDEAS

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    1. Nikhil Padhye & Piyush Bhardawaj & Kalyanmoy Deb, 2013. "Improving differential evolution through a unified approach," Journal of Global Optimization, Springer, vol. 55(4), pages 771-799, April.
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    Cited by:

    1. Umesh Balande & Deepti Shrimankar, 2019. "SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems," Mathematics, MDPI, vol. 7(3), pages 1-26, March.
    2. Fernanda Nakano Kazama & Aluizio Fausto Ribeiro Araujo & Paulo Barros Correia & Elaine Guerrero-Peña, 2021. "Constraint-guided evolutionary algorithm for solving the winner determination problem," Journal of Heuristics, Springer, vol. 27(6), pages 1111-1150, December.
    3. Amir H. Gandomi & Ali R. Kashani, 2018. "Probabilistic evolutionary bound constraint handling for particle swarm optimization," Operational Research, Springer, vol. 18(3), pages 801-823, October.

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