Why Tuning the Control Parameters of Metaheuristic Algorithms Is So Important for Fair Comparison?

  • Anezka Kazikova Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic
  • Michal Pluhacek Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic
  • Roman Senkerik Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic
Keywords: Parameter tuning, metaheuristics, comparison, swarm algorithms, configuration, particle swarm optimization.

Abstract

Although metaheuristic optimization has become a common practice, new bio-inspired algorithms often suffer from a priori ill reputation. One of the reasons is a common bad practice in metaheuristic proposals. It is essential to pay attention to the quality of conducted experiments, especially when comparing several algorithms among themselves. The comparisons should be fair and unbiased. This paper points to the importance of proper initial parameter configurations of the compared algorithms. We highlight the performance differences with several popular and recommended parameter configurations. Even though the parameter selection was mostly based on comprehensive tuning experiments, the algorithms' performance was surprisingly inconsistent, given various parameter settings. Based on the presented evidence, we conclude that paying attention to the metaheuristic algorithm's parameter tuning should be an integral part of the development and testing processes.

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Published
2020-12-21
How to Cite
[1]
KazikovaA., PluhacekM. and SenkerikR. 2020. Why Tuning the Control Parameters of Metaheuristic Algorithms Is So Important for Fair Comparison?. MENDEL. 26, 2 (Dec. 2020), 9-16. DOI:https://doi.org/10.13164/mendel.2020.2.009.
Section
Articles