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Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms

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  • Kalyanmoy Deb
  • Nikhil Padhye

Abstract

Evolutionary Algorithms (EAs) are emerging as competitive and reliable techniques for several optimization tasks. Juxtapositioning their higher-level and implicit correspondence; it is provocative to query if one optimization algorithm can benefit from another by studying underlying similarities and dissimilarities. This paper establishes a clear and fundamental algorithmic linking between particle swarm optimization (PSO) algorithm and genetic algorithms (GAs). Specifically, we select the task of solving unimodal optimization problems, and demonstrate that key algorithmic features of an effective Generalized Generation Gap based Genetic Algorithm can be introduced into the PSO by leveraging this algorithmic linking while significantly enhance the PSO’s performance. However, the goal of this paper is not to solve unimodal problems, neither is to demonstrate that the modified PSO algorithm resembles a GA, but to highlight the concept of algorithmic linking in an attempt towards designing efficient optimization algorithms. We intend to emphasize that the evolutionary and other optimization researchers should direct more efforts in establishing equivalence between different genetic, evolutionary and other nature-inspired or non-traditional algorithms. In addition to achieving performance gains, such an exercise shall deepen the understanding and scope of various operators from different paradigms in Evolutionary Computation (EC) and other optimization methods. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • 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.
    • Handle: RePEc:spr:coopap:v:57:y:2014:i:3:p:761-794
    DOI: 10.1007/s10589-013-9605-0
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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. 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).
    2. Arnaud Flori & Hamouche Oulhadj & Patrick Siarry, 2022. "QUAntum Particle Swarm Optimization: an auto-adaptive PSO for local and global optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 525-559, June.
    3. Mohamed R. Torkomany & Hassan Shokry Hassan & Amin Shoukry & Mohamed Hussein & Chihiro Yoshimura & Mohamed Elkholy, 2023. "Investigation of Optimum Sustainable Designs for Water Distribution Systems from Multiple Economic, Operational, and Health Perspectives," Sustainability, MDPI, vol. 15(2), pages 1-17, January.

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