Abstract
Metaheuristic Search is a successful strategy for solving optimization problems, leading to over two hundred published metaheuristic algorithms. Consequently, there is an interest in understanding the similarities between metaheuristics. Previous studies have done theoretical analyses based on components and search strategies, providing insights into the relationship between different algorithms. In this paper, we argue that it is also important to consider the classes of optimization problems that the algorithms are capable of solving. To this end, we propose a method to measure the similarity between metaheuristics based on their performance on a set of optimization functions. We then use the proposed method to analyze the similarity between different algorithms as well as the similarity between the same algorithm but with different parameter settings. Our method can show if parameter settings of the same algorithm are more similar between themselves than to other algorithms and suggest a clustering based on the performance profile.
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Notes
- 1.
Code available at the following Github repository: https://github.com/jair-pereira/mhcmp/tree/bioma2022.
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Pereira Junior, J., Aranha, C. (2022). Empirical Similarity Measure for Metaheuristics. In: Mernik, M., Eftimov, T., Črepinšek, M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2022. Lecture Notes in Computer Science, vol 13627. Springer, Cham. https://doi.org/10.1007/978-3-031-21094-5_6
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