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

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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.

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Notes

  1. The source codes adopted in this paper are available at [33], or by e-mailing npdhye@gmail.com.

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Acknowledgements

The first author acknowledges the start-up grant provided by Department of Electrical and Computer Engineering and College of Engineering, Michigan State University, East Lansing during the course of this study. Second author acknowledges discussions with Professor C.K. Mohan during his visits at Syracuse University on Particle Swarm Optimization.

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  1. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA

    Kalyanmoy Deb

  2. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Nikhil Padhye

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  1. Kalyanmoy Deb
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  2. Nikhil Padhye
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Correspondence to Kalyanmoy Deb.

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Deb, K., Padhye, N. Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms. Comput Optim Appl 57, 761–794 (2014). https://doi.org/10.1007/s10589-013-9605-0

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