IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v62y2015i3p851-890.html
   My bibliography  Save this article

Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10589-015-9752-6
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10589-015-9752-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Feng, Yanling & Li, Guo & Sethi, Suresh P., 2018. "A three-layer chromosome genetic algorithm for multi-cell scheduling with flexible routes and machine sharing," International Journal of Production Economics, Elsevier, vol. 196(C), pages 269-283.
    2. Thang Trung Nguyen & Nguyen Vu Quynh & Minh Quan Duong & Le Van Dai, 2018. "Modified Differential Evolution Algorithm: A Novel Approach to Optimize the Operation of Hydrothermal Power Systems while Considering the Different Constraints and Valve Point Loading Effects," Energies, MDPI, vol. 11(3), pages 1-30, March.
    3. Kalyanmoy Deb & Nikhil Padhye, 2014. "Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms," Computational Optimization and Applications, Springer, vol. 57(3), pages 761-794, April.
    4. Yin, Xiuxing & Zhao, Xiaowei & Lin, Jin & Karcanias, Aris, 2020. "Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations," Energy, Elsevier, vol. 202(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:coopap:v:62:y:2015:i:3:p:851-890. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.