Rethinking Metaheuristics: Unveiling the Myth of “Novelty” in Metaheuristic Algorithms - Chia-Hung Wang; Kun Hu; Xiaojing Wu; Yufeng Ou (Mathematics, 2025)
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.