I have found that most of the single objective metaheuristic algorithms perform quick better than that of the traditional GA, EA, PSO, and so on.
But when they were improved with archive-method, decomposition-method, or indicator-method ways, the revised algorithm solved the multiple objective benchmark functions quick worse than that of the NSGA-II, NSGA-III, MOPSO, SPEA2, and so on.
Why?
Are you _sure_ they really "perform quick better"?
You may read papers like:
Camacho-Villalón, Christian L., Marco Dorigo, and Thomas Stützle. ‘Exposing the Grey Wolf, Moth-Flame, Whale, Firefly, Bat, and Antlion Algorithms: Six Misleading Optimization Techniques Inspired by Bestial Metaphors’. International Transactions in Operational Research, July 2022. https://doi.org/10.1111/itor.13176
Wang, Chia-Hung, Kun Hu, Xiaojing Wu, and Yufeng Ou. 2025. “Rethinking Metaheuristics: Unveiling the Myth of ‘Novelty’ in Metaheuristic Algorithms.” Mathematics 13 (July):2158. https://doi.org/10.3390/math13132158.
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