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

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 c...

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Bibliographic Details
Published in:Mendel (Brno (Czech Republic)) Vol. 26; no. 2; pp. 9 - 16
Main Authors: Kazikova, Anezka, Pluhacek, Michal, Senkerik, Roman
Format: Journal Article
Language:English
Published: Brno University of Technology 01.12.2020
Subjects:
comparison
configuration
metaheuristics
Parameter tuning
particle swarm optimization
swarm algorithms
ISSN:1803-3814, 2571-3701
Online Access:Get full text
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Summary: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.
ISSN:1803-3814
2571-3701
DOI:10.13164/mendel.2020.2.009