More is not Always Better: Insights from a Massive Comparison of Meta-heuristic Algorithms over Real-Parameter Optimization Problems
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2021
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Institute of Electrical and Electronics Engineers Inc.
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Much controversy has been lately risen around the design and performance of modern bio-inspired optimization methods, in particular due to the alleged lack of algorithmic novelty in their definition with respect to traditional heuristic solvers. In this work we present a first attempt at shedding empirical evidence over this debate, for which results of a benchmark with unprecedented scales in terms of problems and algorithms are reported and discussed. Specifically, informed conclusions are held in what refers to the claimed superior performance of these bio-inspired solvers and their competitiveness when compared to competition-winning alternatives. Finally, we prove that the tailored selection of a subset of problems and techniques can unfairly bias the comparisons favoring any of such algorithms, ultimately arriving at illusory conclusions about their comparative performance.
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Publisher Copyright: © 2021 IEEE.
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Del Ser , J , Osaba , E , Martinez , A D , Bilbao , M N , Poyatos , J , Molina , D & Herrera , F 2021 , More is not Always Better : Insights from a Massive Comparison of Meta-heuristic Algorithms over Real-Parameter Optimization Problems . in 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings . 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings , Institute of Electrical and Electronics Engineers Inc. , 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 , Orlando , United States , 5/12/21 . https://doi.org/10.1109/SSCI50451.2021.9660030
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