Rethinking Metaheuristics: Unveiling the Myth of “Novelty” in Metaheuristic Algorithms
Chia-Hung Wang (),
Kun Hu,
Xiaojing Wu and
Yufeng Ou
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Chia-Hung Wang: College of Computer Science and Mathematics, Fujian University of Technology, No. 69, Xuefu South Road, Fuzhou 350118, China
Kun Hu: College of Computer Science and Mathematics, Fujian University of Technology, No. 69, Xuefu South Road, Fuzhou 350118, China
Xiaojing Wu: College of Electronics, Electrical Engineering and Physics, Fujian University of Technology, No. 69, Xuefu South Road, Fuzhou 350118, China
Yufeng Ou: College of Computer Science and Mathematics, Fujian University of Technology, No. 69, Xuefu South Road, Fuzhou 350118, China
Mathematics, 2025, vol. 13, issue 13, 1-28
Abstract:
In recent decades, the rapid development of metaheuristic algorithms has outpaced theoretical understanding, with experimental evaluations often overshadowing rigorous analysis. While nature-inspired optimization methods show promise for various applications, their effectiveness is often limited by metaphor-driven design, structural biases, and a lack of sufficient theoretical foundation. This paper systematically examines the challenges in developing robust, generalizable optimization techniques, advocating for a paradigm shift toward modular, transparent frameworks. A comprehensive review of the existing limitations in metaheuristic algorithms is presented, along with actionable strategies to mitigate biases and enhance algorithmic performance. Through emphasis on theoretical rigor, reproducible experimental validation, and open methodological frameworks, this work bridges critical gaps in algorithm design. The findings support adopting scientifically grounded optimization approaches to advance operational applications.
Keywords: optimization algorithm; optimization; metaphors; structural bias; evaluation of algorithm’s innovation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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