Reference

Critical Papers to Some Metaphors-Based Optimization

Most (but not all) of metaphors-based optimizers are highly criticized by more and more scholars, researchers, and participant‌s.

Collections: EC-Bestiary from Felipe Campelo (University of Bristol) et al.

• 2026: Beyond metaphors: Rethinking metaphors in metaheuristics algorithm design

• 2025: Structural bias in metaheuristic algorithms: Insights, open problems, and future prospects https://doi.org/10.1016/j.swevo.2024.101812

• 2025: On the structural and statistical flaws of the * optimizer https://doi.org/10.48550/arXiv.2511.17557

• 2025: The paradox of success in evolutionary and bioinspired optimization: Revisiting critical issues, key studies, and methodological pathways https://doi.org/10.48550/arXiv.2501.07515

• 2024: Research orientation and novelty discriminant for new metaheuristic algorithms https://doi.org/10.1016/j.asoc.2024.111521

• 2024: Metaheuristics exposed: Unmasking the design pitfalls of * optimization algorithm in benchmarking https://doi.org/10.1016/j.asoc.2024.111696

• 2024: Comprehensive taxonomies of nature- and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis and recommendations (from 2020 to 2024) https://doi.org/10.1007/s12559-020-09730-8

• 2024: Exposing the * optimization algorithm: A misleading metaheuristic technique with structural bias https://doi.org/10.1016/j.asoc.2024.111574

• 2024: A literature review and critical analysis of metaheuristics recently developed https://doi.org/10.1007/s11831-023-09975-0

• 2023: Does the field of nature-Inspired computing contribute to achieving lifelike features https://doi.org/10.1162/artl_a_00407

• 2023: Exposing the *, *, *, *, *, and * algorithms: Six misleading optimization techniques inspired by bestial metaphors https://doi.org/10.1111/itor.13176

• 2022: A new taxonomy of global optimization algorithms https://doi.org/10.1007/s11047-020-09820-4

• 2022: Metaphor-based metaheuristics, a call for action: The elephant in the room https://doi.org/10.1007/s11721-021-00202-9

• 2020: Nature inspired optimization algorithms or simply variations of metaheuristics? https://doi.org/10.1007/s10462-020-09893-8

• 2020: Benchmarking in optimization: Best practice and open issues https://doi.org/10.48550/arXiv.2007.03488

• *, * and * algorithms: Three widespread algorithms that do not contain any novelty https://doi.org/10.1007/978-3-030-60376-2_10

• 2019: The * algorithm: why it cannot be considered a novel algorithm: A brief discussion on the use of metaphors in optimization https://doi.org/10.1007/s11721-019-00165-y

• 2019: Bio-inspired computation: Where we stand and what’s next https://doi.org/10.1016/j.swevo.2019.04.008

• 2018: An insight into bio-inspired and evolutionary algorithms for global optimization: Review, analysis, and lessons learnt over a decade of competitions https://doi.org/10.1007/s12559-018-9554-0

• 2015: A critical analysis of the * search algorithm — How not to solve sudoku https://doi.org/10.1016/j.orp.2015.04.001

• 2014: How novel is the “novel” * optimization approach? https://doi.org/10.1016/j.ins.2014.01.026

• 2011: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms https://doi.org/10.1016/j.swevo.2011.02.002

• 2011: Analytical and numerical comparisons of *-based optimization and genetic algorithms https://doi.org/10.1016/j.ins.2010.12.006

• 2010: A rigorous analysis of the * search algorithm: How the research community can be misled by a “novel” methodology https://doi.org/10.4018/jamc.2010040104