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Probabilistic evolutionary bound constraint handling for particle swarm optimization

Author

Listed:
  • Amir H. Gandomi

    (Stevens Institute of Technology)

  • Ali R. Kashani

    (Arak University)

Abstract

Keeping the search space between the valid domains is one of the most important necessities for most of the optimization problems. Among the optimization algorithms, particle swarm optimization (PSO) is highly likely to violate boundary limitations easily because of its oscillating behavior. Therefore, PSO is led to be sensitive to bound constraint handling (BCH) method. This matter has not been taken to account very much until now. This study attempt to apply and explore the efficiency of one of the most recent BCH schemes called evolutionary boundary constraint handling (EBCH) on PSO. In addition, probabilistic evolutionary boundary constraint handling (PEBCH) is also introduced in this study as an update on EBCH approach. As a complementary step of previous efforts, in the current document, PSO with both EBCH and PEBCH are utilized to solve several benchmark functions and the results are compared to other approaches in the literature. The results reveal that, in most cases, the EBCH and PEBCH can considerably improve the performance of the PSO algorithm in comparison with other BCH methods.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:operea:v:18:y:2018:i:3:d:10.1007_s12351-018-0401-6
    DOI: 10.1007/s12351-018-0401-6
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Sina Nayeri & Zeinab Sazvar & Jafar Heydari, 2022. "A fuzzy robust planning model in the disaster management response phase under precedence constraints," Operational Research, Springer, vol. 22(4), pages 3571-3605, September.

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