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Boosting galactic swarm optimization with ABC

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Abstract

Galactic swarm optimization (GSO) is a new global metaheuristic optimization algorithm. It manages multiple sub-populations to explore search space efficiently. Then superswarm is recruited from the best-found solutions. Actually, GSO is a framework. In this framework, search method in both sub-population and superswarm can be selected differently. In the original work, particle swarm optimization is used as the search method in both phases. In this work, performance of the state of the art and well known methods are tested under GSO framework. Experiments show that performance of artificial bee colony algorithm under the GSO framework is the best among the other algorithms both under GSO framework and original algorithms.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Konya Technical University, Konya, Turkey

    Ersin Kaya, Sait Ali Uymaz & Baris Kocer

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  1. Ersin Kaya
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  2. Sait Ali Uymaz
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Kaya, E., Uymaz, S.A. & Kocer, B. Boosting galactic swarm optimization with ABC. Int. J. Mach. Learn. & Cyber. 10, 2401–2419 (2019). https://doi.org/10.1007/s13042-018-0878-6

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  • DOI: https://doi.org/10.1007/s13042-018-0878-6

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