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On Selection of a Benchmark by Determining the Algorithms' Qualities

Abstract: The authors got the motivation for writing the article based on an issue, with which developers of the newly developed nature-inspired algorithms are usually confronted today: How to select the test benchmark such that it highlights the quality of the developed algorithm most fairly? In line with this, the CEC Competitions on Real-Parameter Single-Objective Optimization benchmarks that were issued several times in the last decade, serve as a testbed for evaluating the collection of nature-inspired algorithms selected in our study. Indeed, this article addresses two research questions: (1) How the selected benchmark affects the ranking of the particular algorithm, and (2) If it is possible to find the best algorithm capable of outperforming all the others on all the selected benchmarks. Ten outstanding algorithms (also winners of particular competitions) from different periods in the last decade were collected and applied to benchmarks issued during the same time period. A comparative analysis showed that there is a strong correlation between the rankings of the algorithms and the benchmarks used, although some deviations arose in ranking the best algorithms. The possible reasons for these deviations were exposed and commented on.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Fuente: IEEE Access 2021, 9, 51166 - 51178

Editorial: Institute of Electrical and Electronics Engineers, Inc.

 Año de publicación: 2021

Nº de páginas: 13

Tipo de publicación: Artículo de Revista

 DOI: 10.1109/ACCESS.2021.3058285

ISSN: 2169-3536

Proyecto español: TIN2017-89275-R.

Proyecto europeo: info:eu-repo/grantAgreement/EC/H2020/778035/EU/PDE-based geometric modelling, image processing, and shape reconstruction/PDE-GIR/

Url de la publicación: http://doi.org/10.1109/ACCESS.2021.3058285.