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JAIT 2022 Vol.13(4): 374-380
doi: 10.12720/jait.13.4.374-380

Optimization of Artificial Landscapes with a Hybridized Firefly Algorithm

Kevin Saner, Kyle Smith, Thomas Hanne, and Rolf Dornberger
School of Business, University of Applied Sciences and Arts Northwestern Switzerland, Olten/Basel, Switzerland

Abstract—This paper shows how the metaheuristic Firefly Algorithm (FA) can be enhanced by hybridization with a genetic algorithm to achieve better results for optimization problems. The authors examine which configuration of the hybridized FA performs best during a number of computational tests. The performance of the hybrid FA is compared with that of the regular FA in solving test functions for single-objective optimization problems in two and n-dimensional spaces. The key findings are that more complex optimization problems benefit from the hybrid FA because it outperforms the basic FA. In addition, some useful parameters settings for the suggested algorithm are determined.
Index Terms—metaheuristics, swarm intelligence, firefly algorithm, genetic algorithm, hybridization, single-objective optimization, artificial landscapes, performance evaluation

Cite: Kevin Saner, Kyle Smith, Thomas Hanne, and Rolf Dornberger, "Optimization of Artificial Landscapes with a Hybridized Firefly Algorithm," Journal of Advances in Information Technology, Vol. 13, No. 4, pp. 374-380, August 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.