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Conceptual and numerical comparisons of swarm intelligence optimization algorithms

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Abstract

Swarm intelligence (SI) optimization algorithms are fast and robust global optimization methods, and have attracted significant attention due to their ability to solve complex optimization problems. The underlying idea behind all SI algorithms is similar, and various SI algorithms differ only in their details. In this paper we discuss the algorithmic equivalence of particle swarm optimization (PSO) and various other newer SI algorithms, including the shuffled frog leaping algorithm (SFLA), the group search optimizer (GSO), the firefly algorithm (FA), artificial bee colony algorithm (ABC) and the gravitational search algorithm (GSA). We find that the original versions of SFLA, GSO, FA, ABC, and GSA, are all algorithmically identical to PSO under certain conditions. We discuss their diverse biological motivations and algorithmic details as typically implemented, and show how their differences enhance the diversity of SI research and application. Then we numerically compare SFLA, GSO, FA, ABC, and GSA, with basic and advanced versions on some continuous benchmark functions and combinatorial knapsack problems. Empirical results show that an advanced version of ABC performs best on the continuous benchmark functions, and advanced versions of SFLA and GSA perform best on the combinatorial knapsack problems. We conclude that although these SI algorithms are conceptually equivalent, their implementation details result in notably different performance levels.

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Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. 1344954, the National Natural Science Foundation of China under Grant Nos. 61305078, 61533010, 61179041.

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Authors and Affiliations

  1. Department of Electrical Engineering, Shaoxing University, Shaoxing, Zhejiang, China

    Haiping Ma & Sengang Ye

  2. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China

    Haiping Ma & Minrui Fei

  3. Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH, USA

    Dan Simon

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  1. Haiping Ma
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  2. Sengang Ye
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  3. Dan Simon
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  4. Minrui Fei
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Correspondence to Haiping Ma.

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Communicated by V. Loia.

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Ma, H., Ye, S., Simon, D. et al. Conceptual and numerical comparisons of swarm intelligence optimization algorithms. Soft Comput 21, 3081–3100 (2017). https://doi.org/10.1007/s00500-015-1993-x

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