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REVIEW 3 major objections 6 minor 82 references

The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper argues that bioinspired optimization's publishing boom masks a core crisis: most new solvers are minor variants of three classical algorithms.

desk verdict A fair, well-organized synthesis of known critiques; the 'more than half' claim is the soft spot, but the paper earns a review. read the letter →

arxiv 2501.07515 v1 pith:CCBZA6AS submitted 2025-01-13 cs.NE cs.AI

Daniel Molina , Javier Del Ser , Javier Poyatos , Francisco Herrera This is my paper · ORCID
classification cs.NEcs.AI MSC 68W5090C59
keywords BioinspiredcomputationEvolutionaryMetaheuristicsMethodologicalcritiqueBenchmarkingAlgorithmicinnovationMetaphor-basedalgorithmsAutomatedalgorithmdesign
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that evolutionary and bioinspired optimization are in a 'paradox of success': publication volume has grown enormously, but the field's core is weakened by inadequate benchmarking, problem-specific overfitting, thin theoretical grounding, and proposals whose only justification is a biological metaphor. It argues, citing its own taxonomy, that more than half of the reviewed nature-inspired solvers are incremental, minor versions of just three classical algorithms—Particle Swarm Optimization, Differential Evolution, and Genetic Algorithms. The review recasts the accumulated critical literature as a constructive diagnosis and collects the methodological pathways proposed to fix it: detecting weak proposals through equation-level and configuration-level equivalence, fair and replicable benchmarking, real-world-relevant test problems, and automated algorithm design. A sympathetic reader should care because the paper is trying to say that the field can keep its momentum only by making novelty and experimental rigor the explicit standard for publication. If the diagnosis is right, a large share of published solvers are not contributions but recycled designs, and remediation is a community-level editorial and methodological project rather than a technical tweak.

What carries the argument

The analytical engine is a two-layer recapitulation. The first layer is the taxonomy of [19], which classifies solvers by inspiration versus algorithmic behavior and supplies the quantitative claim that most proposals are incremental variants of PSO, DE, and GA. The second layer is the pair of equivalence criteria proposed for separating 'wheat from chaff': equation-level equivalence, which compares search operators mathematically at the operator or component level (through homologous-component studies, formal verification, operator simplification, and ablation tests), and configuration-level equivalence, which asks whether parameter settings make a new solver behave essentially like an existing one. These criteria do the work of turning the critical literature into an actionable test: a proposal that is equation- or configuration-equivalent to an existing solver is non-innovative, one that generalizes an existing solver makes the old version redundant, and only a solver whose behavior cannot be replicated by adjusting parameters counts as genuinely innovative. The review also presents benchmarking and replicability standards, and automated design (with and without large language models) as the constructive forward path.

What would settle it

A random or systematic audit of a sizable sample of published bioinspired algorithms—formalizing each solver's search operators and testing equation-level and configuration-level equivalence against modern PSO, DE, and GA—that found the majority are not equivalent to those three classical families would refute the field-level claim, though individual case studies like Harmony Search or Black Hole Optimization could still stand.

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Extended reading notes

Core claim

The paper's central claim, stated in the abstract and developed through the review, is that both areas are 'plagued by challenges at their core': a lack of algorithmic innovation, low-quality experimental studies, and poor benchmarks, with a steady flow of 'superfluous proposals justified only by their biological metaphor.' Section 2.1 commits the paper to the stronger empirical estimate from its own taxonomy that more than half of the proposals reviewed are incremental, minor versions of only three very classical algorithms (Particle Swarm Optimization, Differential Evolution, and Genetic Algorithms). The review then assembles the case-study literature exposing individual weak proposals—Harmony Search as essentially a special case of Evolution Strategies, Black Hole Optimization as a simplification of Particle Swarm Optimization, and Grey Wolf, Firefly, and Bat algorithms as reformulations of existing PSO variants—and converts these critiques into positive pathways for detecting equivalence, benchmarking fairly, and automating algorithm design. It is not an experimental demonstration; it is an argument that the field's success in volume has not produced corresponding innovation, and that the proposed pathways are the right remedy.

Load-bearing premise

The load-bearing premise is that the criticized algorithms (Harmony Search, Black Hole Optimization, Grey Wolf Optimizer, and similar cases) are typical of the field rather than hand-picked weak examples, so that the paradox of success is a systemic property and not just a curated list.

Editorial extensions

If this is right

  • If the taxonomy's estimate holds, any new bioinspired solver should be compared against modern, well-tuned versions of Particle Swarm Optimization, Differential Evolution, and Genetic Algorithms rather than naive classical baselines, because beating the naive versions is easy and proves little.
  • Equation-level and configuration-level equivalence tests give reviewers and editors a concrete procedure to classify a proposal as non-innovative, making the open letter's editorial demands operational.
  • Because many popular solvers carry a bias toward the center of the search domain, experiments on shifted and diverse benchmark functions are necessary to avoid reporting a bias artifact as a performance gain.
  • Replicability requirements such as extensible templates, white-box problem descriptions, and remotely accessible frameworks would make published results externally verifiable, weakening the publish-or-perish incentives behind metaphor-based method proposals.
  • If automated design, including LLM-driven generation, becomes the norm, the biological metaphor stops being the criterion of novelty and the field's value shifts toward solving real-world problems, which the paper states as its ultimate goal.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the paper leaves implicit is to run its equivalence-detection machinery over a large random sample of the 500+ solvers already classified in its taxonomy and publish the distribution of equivalence classes; that would calibrate the 'more than half' estimate with formal methods rather than expert judgment.
  • The configuration-level criterion implies a stronger practical corollary: for every allegedly novel solver, some parameterized portfolio of the classical algorithms could match its behavior across a benchmark suite, so the practical contribution of many published solvers would reduce to tuning rather than search logic.
  • The same audit could be adapted to neighboring publication-heavy fields in machine learning where novelty claims are name- or metaphor-driven, although the equivalence formalisms would need to be reworked beyond population-based search.
  • A community-level testable design extension is to make automated-design software frameworks the default venue for proposing solvers, so that novelty is demonstrated by verifiable performance on real-world problems rather than by a new metaphor.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This manuscript is a review/position paper on methodological rigor in evolutionary and bioinspired optimization. It argues that the field exhibits a 'paradox of success': the large and growing volume of publications proposing nature- or bio-inspired solvers has not produced commensurate algorithmic innovation. Section 2 organizes known critiques into three categories: lack of algorithmic novelty, low-quality experimental comparisons, and poor or biased benchmarks. Sections 3-5 present pathways for detecting weak proposals (equation-level and configuration-level equivalence tests), for fair and replicable benchmarking, and for improving existing solvers. Section 6 discusses automated and LLM-based algorithm design as a promising direction. The authors conclude by urging researchers, reviewers, and editors to adopt the proposed guidelines. The paper's contribution is synthesis and normative guidance rather than new empirical evidence.

Significance. If accepted as a synthesis, the paper is a useful and well-organized digest of a dispersed critical literature. Its strengths include accurate quotation and attribution of key critiques, explicit translation of those critiques into concrete equivalence and benchmarking procedures, and practical recommendations for authors and editors. It also points to immediate tools such as ablation tests, origin-bias tests, and formal verification. The main limitation is that the paper does not itself establish the prevalence of weak proposals: the 'more than half' statistic is inherited from the authors' prior taxonomy, and the proposed pathways are not evaluated against outcome data. These limits affect the strength of the systemic diagnosis but do not negate the value of the review as a roadmap.

major comments (3)
  1. [Section 2.1, quoted passage from [19]] The abstract and Section 2.1 treat the claim that 'more than half of the proposals reviewed in our work are incremental, minor versions of only three very classical algorithms' as a factual characterization of the field. This statistic is quoted from the authors' own taxonomy [19], but the present manuscript does not report the operational definition of 'incremental, minor version,' the inclusion criteria of the reviewed corpus, inter-rater reliability of the classification, or any independent replication. Since this proportion is the empirical backbone of the 'paradox of success' and motivates the call for editorial policy changes, the authors should either provide additional evidence for the representativeness of this estimate or explicitly restate it as a hypothesis/estimate from one classification study, with a discussion of how selection bias in that corpus could change the conclusion.
  2. [Sections 2.1 and 3.1, selection of critical case studies] The critical examples (Harmony Search, Black Hole Optimization, Grey Wolf Optimizer, Raven Roost Optimization, and others) are presented as evidence of weak proposals, but the paper does not describe how these particular studies were selected from the wider critical literature. No systematic search strategy, inclusion/exclusion criteria, or temporal coverage is given. Without this, the reader cannot distinguish a systemic, field-wide phenomenon from a list of deliberately chosen pathologies. The authors should add a short methodology paragraph or a table of all analyzed critical studies with their targets and methods so that the representativeness of the synthesis can be assessed.
  3. [Sections 3.2 and 6, proposed pathways as remedies] The paper presents equation-level equivalence, configuration-level equivalence, and automated design as 'methodological pathways' that will refocus the field, and Section 7 states that 'the analyzed pathways are designed to refocus optimization research on its ultimate goal.' Yet no evidence is offered that applying these pathways changes editorial decisions, prevents publication of weak solvers, or improves algorithm design. For instance, no case is shown where an equivalence screen detected a non-innovative algorithm before publication, and the LLM-based systems in Section 6.2 are described without an evaluation of whether their outputs satisfy the equivalence and benchmarking criteria advocated earlier. The pathways may be reasonable prescriptions, but the manuscript should either soften these claims to 'proposals to be validated' or cite pilot studies that demonstrate their efficacy.
minor comments (6)
  1. [Section 2.2] The sentence beginning 'However, this is not the case in many of such studies. as underscored in [19]:' has a punctuation error; 'as' should not begin a new sentence after the period.
  2. [Section 3.1] The sentence 'A comprehensive component-based analysis of each algorithm to substantiate this assertion, these algorithms are identified as variants of Particle Swarm Optimization and Evolution Strategies.' is grammatically broken and should be rewritten.
  3. [Section 3.2] The phrase 'straw-grain discrimination studies' is unclear; consider 'wheat-from-chaff discrimination' or 'differentiating weak from strong proposals.'
  4. [References] Reference [13] contains a malformed DOI ('https://doi.org/10.1109/10.1016/j.asoc.2023.110757'); the DOI should be corrected or the URL cleaned.
  5. [Sections 2.1 and 5] The paper reports inherited quantitative claims ('more than half' in Section 2.1 and '65%' in Section 5) without a summary table; adding a table with the source, method, and study corpus for each quantitative claim would make the evidence easier to verify.
  6. [Section 2.1] The statement that 'almost a hundred important researchers' signed the open letter [24] should be updated with the exact number or a citation to a source that tracks the count, since the number may have changed since the letter's initial publication.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a literature-based overview whose diagnostic claims rest on external critiques and a separate prior taxonomy, not on any fit or self-referential derivation.

full rationale

This is a review and position paper, not a derivation or prediction paper. It contains no fitted parameters, no equations whose outputs equal inputs, and no empirical result that is renamed as a prediction. The central claim—that much published bioinspired and evolutionary optimization research suffers from weak novelty, poor benchmarking, and insufficient rigor—is supported by a broad set of independent prior works (Sørensen, Weyland, Piotrowski et al., Camacho-Villalón et al., Kudela, the open letter by Aranha et al.), rather than by a derivation from the paper's own assumptions. The most specific quantitative statement, that "more than half of the proposals reviewed in our work are incremental, minor versions of only three very classical algorithms," is quoted from the authors' own taxonomy [19]. This is a self-citation, but it is not load-bearing circularity: [19] is a separately published corpus study that classifies more than 500 algorithms, it does not encode the present paper's conclusions, and the same general diagnosis is corroborated by external studies such as [22], [33], [53], and [57]. The proposed pathways in Sections 3.2, 4, and 6 are explicitly recommendations for future research practice; the paper does not claim to have validated their effectiveness, so there is no fitted-input-called-prediction pattern and no circular reduction. The "paradox of success" is an organizing label for previously published critiques, not a new result derived from that label. Overall, the argument is self-contained as a critical overview, and no derivation chain reduces to its own inputs.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters or invented entities appear because the paper makes no quantitative claims. The central claim rests on the representativeness of the surveyed critiques, the measurability of algorithmic novelty, the normative goal of real-world problem solving, and the promise of automated design.

assumptions (4)
  • domain assumption The surveyed critical literature is representative of bioinspired optimization research as a whole.
    The 'paradox of success' is inferred from a curated set of critiques and case studies in Sections 2.1 and 3.1. If these are atypical, the central diagnosis overgeneralizes.
  • domain assumption Algorithmic novelty can be objectively assessed via the proposed equivalence and component-based methods.
    Section 3.2 presumes that equation-level and configuration-level equivalence are sufficient to classify a proposal as non-innovative, which is itself a methodological position.
  • domain assumption The primary objective of metaheuristic research is solving real-world problems.
    The recommendations in Sections 4.1 and 7 rest on this normative goal. A researcher focused on theoretical understanding might weigh other criteria.
  • domain assumption Automated design, including LLM-based design, can produce algorithms at least as good as manual design.
    Section 6 presents automated design as a 'smart and promising solution', but it cites only early promising results, not a settled equivalence.

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Cite this review

Pith. "Pith review of The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways." pith.science (2026). https://pith.science/paper/CCBZA6AS

@misc{pith2026250107515,
  author       = {Pith},
  title        = {Pith review of: The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CCBZA6AS}},
  note         = {Machine review of arXiv:2501.07515}
}
read the original abstract

Evolutionary and bioinspired computation are crucial for efficiently addressing complex optimization problems across diverse application domains. By mimicking processes observed in nature, like evolution itself, these algorithms offer innovative solutions beyond the reach of traditional optimization methods. They excel at finding near-optimal solutions in large, complex search spaces, making them invaluable in numerous fields. However, both areas are plagued by challenges at their core, including inadequate benchmarking, problem-specific overfitting, insufficient theoretical grounding, and superfluous proposals justified only by their biological metaphor. This overview recapitulates and analyzes in depth the criticisms concerning the lack of innovation and rigor in experimental studies within the field. To this end, we examine the judgmental positions of the existing literature in an informed attempt to guide the research community toward directions of solid contribution and advancement in these areas. We summarize guidelines for the design of evolutionary and bioinspired optimizers, the development of experimental comparisons, and the derivation of novel proposals that take a step further in the field. We provide a brief note on automating the process of creating these algorithms, which may help align metaheuristic optimization research with its primary objective (solving real-world problems), provided that our identified pathways are followed. Our conclusions underscore the need for a sustained push towards innovation and the enforcement of methodological rigor in prospective studies to fully realize the potential of these advanced computational techniques.

Figures

Figures reproduced from arXiv: 2501.07515 by the authors.

Figure 1
Figure 1. Graphical summary of the issues, pathways and promising directions discussed [PITH_FULL_IMAGE:figures/full_fig_p028_1.png] view at source ↗

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Reference graph

Works this paper leans on

82 extracted references · 46 canonical work pages

  1. [19]

    Molina, J

    D. Molina, J. Poyatos, J. D. Ser, S. Garc´ ıa, A. Hussain, F. Herrera, Comprehensive taxonomies of nature-and bio-inspired optimization: In- spiration versus algorithmic behavior, critical analysis recommenda- tions, Cogn. Comput. 12 (2020) 897–939. https://doi.org/10.1007/ s12559-020-09730-8

    work page 2020

  2. [1]

    X.-S. Yang, Z. Cui, R. Xiao, A. H. Gandomi, M. Karamanoglu, Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, Elsevier Science Publishers B. V., NLD, 2013. https://doi.org/10. 1016/B978-0-12-405163-8.00001-6

    2013

  3. [2]

    T. Back, D. B. Fogel, Z. Michalewicz, Handbook of Evolutionary Com- putation, IOP Publishing Ltd., New York, 1997. https://doi.org/10. 1201/9780367802486

    1997

  4. [4]

    Yao, Evolutionary computation: Theory and applications, World Scientific, 1999

    X. Yao, Evolutionary computation: Theory and applications, World Scientific, 1999. https://doi.org/10.1142/2792

    work page doi:10.1142/2792 1999

  5. [5]

    Ashlock, Evolutionary computation for modeling and optimization, volume 571, Springer, New York, 2006

    D. Ashlock, Evolutionary computation for modeling and optimization, volume 571, Springer, New York, 2006. https://doi.org/10.1007/ 0-387-31909-3

    work page 2006

  6. [6]

    Del Ser, E

    J. Del Ser, E. Osaba, D. Molina, X.-S. Yang, S. Salcedo-Sanz, D. Cama- cho, S. Das, P. N. Suganthan, C. A. C. Coello, F. Herrera, Bio-inspired computation: Where we stand and what’s next, Swarm and Evol. Com- put. 48 (2019) 220–250. https://doi.org/10.1016/j.swevo.2019. 04.008. 29

    work page doi:10.1016/j.swevo.2019 2019

  7. [7]

    Al-Sahaf, Y

    H. Al-Sahaf, Y. Bi, Q. Chen, A. Lensen, Y. Mei, Y. Sun, B. Tran, B. Xue, M. Zhang, A Survey on Evolutionary Machine Learning, Journ. of the Royal Society of New Zealand 49 (2019) 205–228. https://doi.org/ 10.1080/03036758.2019.1609052

    work page arXiv 2019

  8. [8]

    Zhan, J.-Y

    Z.-H. Zhan, J.-Y. Li, J. Zhang, Evolutionary deep learning: A sur- vey, Neurocomputing 483 (2022) 42–58. https://doi.org/10.1016/ j.neucom.2022.01.099

    work page 2022

Show all 82 references
  1. [9]

    A. D. Martinez, J. Del Ser, E. Villar-Rodriguez, E. Osaba, J. Poyatos, S. Tabik, D. Molina, F. Herrera, Lights and shadows in evolution- ary deep learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges, Informa- tio...

    2021 doi

  2. [10]

    Triguero, D

    I. Triguero, D. Molina, J. Poyatos, J. Del Ser, F. Herrera, General Pur- pose Artificial Intelligence Systems (GPAIS): Properties, definition, tax- onomy, societal implications and responsible governance, Inf. Fusion 103 (2024) 102135. https://doi.org/10.1016/j.inffus.2023.102135

    2024

  3. [11]

    S. Chen, S. Chen, W. Hou, W. Ding, X. You, EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning, IEEE Trans. on Evol. Comput. 28 (2024) 582–596. https://doi.org/10. 1109/TEVC.2023.3307245

    2024

  4. [12]

    Q. Guo, et al., Connecting Large Language Models with Evolution- ary Algorithms Yields Powerful Prompt Optimizers, in: The Twelfth International Conference on Learning Representations, 2024, pp. 1–24. https://openreview.net/forum?id=ZG3RaNIsO8

    2024

  5. [13]

    Poyatos, D

    J. Poyatos, D. Molina, A. Mart´ ınez-Seras, J. Del Ser, F. Herrera, Multi- objective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness, Appl. Soft Comput. 147 (2023) 110757. https://doi.org/10.1109/10.1016/j. aso...

    2023

  6. [14]

    Stork, A

    J. Stork, A. Eiben, T. Bartz-Beielstein, A new taxonomy of global optimization algorithms, Nat. Comput. 21 (2020) 219–242. https:// doi.org/10.1007/s11047-020-09820-4 . 30

    2020 doi

  7. [15]

    Rajwar, K

    K. Rajwar, K. Deep, S. Das, An exhaustive review of the metaheuristic algorithms for search and optimization: Taxonomy, applications, and open challenges, Artif. Intell. Rev. 56 (2023) 13187–13257. https:// doi.org/10.1007/s10462-023-10470-y

    2023 doi

  8. [16]

    Ferrer, P

    J. Ferrer, P. Delgado-P´ erez, Metaheuristics in a Nutshell, in: Optimising the Software Development Process with Artificial Intelligence, Springer, 2023, pp. 279–307. https://doi.org/10.1007/978-981-19-9948-2_ 10

    2023 doi

  9. [17]

    Mart´ ı, M

    R. Mart´ ı, M. Sevaux, K. S¨ orensen, Fifty years of metaheuristics, In Press at Eur. J. Oper. Res. (2024) 1–18. https://doi.org/10.1016/ j.ejor.2024.04.004

    2024

  10. [18]

    Velasco, H

    L. Velasco, H. Guerrero, A. Hospitaler, A literature review and crit- ical analysis of metaheuristics recently developed, Arch. of Com- put. Methods in Eng. 31 (2024) 125–146. https://doi.org/10.1007/ s11831-023-09975-0

    2024

  11. [20]

    Molina, J

    D. Molina, J. Poyatos, J. D. Ser, S. Garc´ ıa, A. Hussain, F. Herrera, Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommen- dations, 2020. Last Update in 2024 at https://arxiv.org/abs/2002. 08136

    2020

  12. [21]

    Sorensen, Metaheuristics—the metaphor exposed, Int

    K. Sorensen, Metaheuristics—the metaphor exposed, Int. Trans. in Oper. Res. 22 (2015) 3–18. https://doi.org/10.1111/itor.12001

    2015 doi

  13. [22]

    Tzanetos, G

    A. Tzanetos, G. Dounias, Nature inspired optimization algorithms or simply variations of metaheuristics?, Artif. Intell. Rev. 54 (2021) 1841–

    2021

  14. [23]

    Campelo, C

    F. Campelo, C. Aranha, Lessons from the Evolutionary Computation Bestiary, Artif. Life 29 (2023) 421–432. https://doi.org/10.1162/ artl_a_00402. 31

    2023

  15. [24]

    Aranha, C

    C. Aranha, C. L. Camacho Villal´ on, F. Campelo, M. Dorigo, R. Ruiz, M. Sevaux, K. S¨ orensen, T. St¨ utzle, Metaphor-based metaheuristics, a call for action: the elephant in the room, Swarm Intell. 16 (2022) 1–6. https://doi.org/10.1007/s11721-021-00202-9

    2022 doi

  16. [25]

    Z. Hu, Q. Zhang, Y. Wang, Q. Su, Z. Xiong, Research orienta- tion and novelty discriminant for new metaheuristic algorithms, Appl. Soft Comput. 157 (2024) 111521. doi: 10.1016/j.asoc.2024.111521, https://doi.org/10.1016/j.asoc.2024.111521

    2024

  17. [26]

    LaTorre, D

    A. LaTorre, D. Molina, E. Osaba, J. Poyatos, J. Del Ser, F. Herrera, A prescription of methodological guidelines for comparing bio-inspired optimization algorithms, Swarm and Evol. Comput. 67 (2021) 100973. https://doi.org/10.1016/j.swevo.2021.100973

    2021

  18. [27]

    C. L. Camacho-Villal´ on, T. St¨ utzle, M. Dorigo, Designing New Meta- heuristics: Manual Versus Automatic Approaches, Intell. Comput. 2 (2023) 0048. https://doi.org/10.34133/icomputing.0048

    2023 doi

  19. [28]

    Kumar, G

    A. Kumar, G. Wu, M. Z. Ali, Q. Luo, R. Mallipeddi, P. N. Sug- anthan, S. Das, A Benchmark-Suite of real-world constrained multi- objective optimization problems and some baseline results, Swarm and Evol. Comput. 67 (2021) 100961.https://doi.org/10.1016/j.swevo. 2021.100961

    2021

  20. [29]

    Weyland, A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a “Novel” Methodology, Int

    D. Weyland, A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a “Novel” Methodology, Int. Journ. of Appl. Metaheuristic Comput. (IJAMC) 1 (2010) 50–60. doi:10.4018/jamc.2010040104

    2010 doi

  21. [30]

    A. P. Piotrowski, J. J. Napiorkowski, P. M. Rowinski, How novel is the “novel” black hole optimization approach?, Inf. Sci. 267 (2014) 191–200. https://doi.org/10.1016/j.ins.2014.01.026

    2014 doi

  22. [31]

    Z. W. Geem, Research commentary: Survival of the fittest algorithm or the novelest algorithm? the existence reason of the harmony search algorithm, in: Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends, IGI Global, 2012, pp. 84–89. https:...

    2012 doi

  23. [32]

    Weyland, A critical analysis of the harmony search algorithm—how not to solve sudoku, Operations Research Perspectives 2 (2015) 97–105

    D. Weyland, A critical analysis of the harmony search algorithm—how not to solve sudoku, Operations Research Perspectives 2 (2015) 97–105. https://doi.org/10.1016/j.orp.2015.04.001

    2015 doi

  24. [33]

    Fister Jr, U

    I. Fister Jr, U. Mlakar, J. Brest, I. Fister, A new population-based nature-inspired algorithm every month: is the current era coming to the end?, in: Proceedings of the 3rd Student Computer Science Research Conference, 2016, pp. 33–37

    2016

  25. [34]

    L. Jiao, J. Zhao, C. Wang, X. Liu, F. Liu, L. Li, R. Shang, Y. Li, W. Ma, S. Yang, Nature-Inspired Intelligent Computing: A Comprehensive Sur- vey, Res. 7 (2024) 0442. https://doi.org/10.34133/research.0442

    2024 doi

  26. [35]

    L. Deng, S. Liu, Metaheuristics exposed: Unmasking the design pit- falls of arithmetic optimization algorithm in benchmarking, Appl. Soft Comput. 160 (2024) 111696. https://doi.org/10.1016/j.asoc. 2024.111696

    2024

  27. [36]

    Halsema, D

    M. Halsema, D. Vermetten, T. B¨ ack, N. Van Stein, A Critical Analysis of Raven Roost Optimization, in: Proceedings of the Genetic and Evo- lutionary Computation Conference Companion, Association for Com- puting Machinery, 2024, p. 1993–2001. https://dl.acm.org/doi/abs/ 10.114...

    2024

  28. [37]

    Molina, A

    D. Molina, A. LaTorre, F. Herrera, An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions, Cogn. Comput. 10 (2018) 517–544. https://doi.org/10.1007/s12559-018-9554-0

    2018 doi

  29. [38]

    Molina, F

    D. Molina, F. Moreno-Garc´ ıa, F. Herrera, Analysis among winners of different IEEE CEC Competitions on Real-Parameters Optimization: Is There Always Improvement?, in: 2017 IEEE Congress on Evol. Com- put. (CEC), 2017, pp. 805–812. https://doi.org/10.1109/CEC.2017. 7969392

    2017 doi

  30. [39]

    A. P. Piotrowski, J. J. Napiorkowski, A. E. Piotrowska, Choice of Bench- mark Optimization Problems Does Matter, Swarm and Evol. Comput. 83 (2023) 101378. https://doi.org/10.1016/j.swevo.2023.101378. 33

    2023

  31. [40]

    A. P. Piotrowski, J. J. Napiorkowski, Some metaheuristics should be simplified, Inf. Sci. 427 (2018) 32–62. https://doi.org/10.1016/j. ins.2017.10.039

    2018 doi

  32. [41]

    Rajwar, K

    K. Rajwar, K. Deep, Structural bias in metaheuristic algorithms: In- sights, open problems, and future prospects, Swarm and Evol. Comput. 92 (2025) 101812. doi: 10.1016/j.swevo.2024.101812

    2025

  33. [42]

    Kudela, A critical problem in benchmarking and analysis of evolu- tionary computation methods, Nat

    J. Kudela, A critical problem in benchmarking and analysis of evolu- tionary computation methods, Nat. Mach. Intell. 4 (2022) 1238–1245. https://doi.org/10.1038/s42256-022-00579-0

    2022 doi

  34. [43]

    Ceberio, B

    J. Ceberio, B. Calvo, Time to Stop and Think: What kind of research do we want to do?, 2024. https://arxiv.org/abs/2402.08298

    2024 arXiv

  35. [44]

    van der Blom, T

    K. van der Blom, T. M. Deist, V. Volz, M. Marchi, Y. Nojima, B. Nau- joks, A. Oyama, T. Tuˇ sar, Identifying Properties of Real-World Optimi- sation Problems Through a Questionnaire, in: Many-Criteria Optimiza- tion and Decision Analysis: State-of-the-Art, Present Challenges, ...

    2023 doi

  36. [45]

    Hellwig, H.-G

    M. Hellwig, H.-G. Beyer, Benchmarking evolutionary algorithms for single objective real-valued constrained optimization – a critical review, Swarm and Evol. Comput. 44 (2019) 927–944. https://doi.org/10. 1016/j.swevo.2018.10.002

    2019

  37. [46]

    Mersmann, M

    O. Mersmann, M. Preuss, H. Trautmann, Benchmarking Evolution- ary Algorithms: Towards Exploratory Landscape Analysis, in: Paral- lel Problem Solving from Nature, PPSN XI, 2010, pp. 73–82. https: //doi.org/10.1007/978-3-642-15844-5_8

    2010 doi

  38. [47]

    H. Yin, D. Vermetten, F. Ye, T. H. W. B¨ ack, A. V. Kononova, Impact of Spatial Transformations on Landscape Features of CEC2022 Basic Benchmark Problems, 2024. https://arxiv.org/abs/2402.07654

    2024 arXiv

  39. [48]

    C. Chen, Q. Liu, Y. Jing, M. Zhang, S. Cheng, Y. Li, On the repre- sentativeness metric of benchmark problems in numerical optimization, Swarm and Evol. Comput. 91 (2024) 101716. https://doi.org/10. 1016/j.swevo.2024.101716. 34

    2024

  40. [49]

    Osaba, E

    E. Osaba, E. Villar-Rodriguez, J. Del Ser, A. J. Nebro, D. Molina, A. LaTorre, P. N. Suganthan, C. A. Coello Coello, F. Herrera, A Tu- torial on the Design, Experimentation and Application of Metaheuris- tic Algorithms to Real-World Optimization Problems, Swarm and Evol. Compu...

    2021

  41. [50]

    A. P. Piotrowski, J. J. Napiorkowski, A. E. Piotrowska, Metaheuris- tics should be tested on large benchmark set with various numbers of function evaluations, Swarm and Evol. Comput. 92 (2025) 101807. doi:10.1016/j.swevo.2024.101807

    2025

  42. [51]

    Pickard, J

    J. Pickard, J. Carretero, V. Bhavsar, On the convergence and origin bias of the Teaching-Learning-Based-Optimization algorithm, Appl. Soft Comput. 46 (2016) 115–127. https://doi.org/10.1016/j.asoc. 2016.04.029

    2016 doi

  43. [52]

    GSA: a gravitational search algorithm

    M. Gauci, T. J. Dodd, R. Groß, Why “GSA: a gravitational search algorithm” is not genuinely based on the law of gravity, Nat. Comput. 11 (2012) 719–720. https://doi.org/10.1007/s11047-012-9322-0

    2012 doi

  44. [53]

    C. L. Camacho Villal´ on, T. St¨ utzle, M. Dorigo, Grey Wolf, Firefly and Bat Algorithms: Three Widespread Algorithms that Do Not Contain Any Novelty, in: M. Dorigo, et al. (Eds.), International Conference on Swarm Intelligence, volume 12421, Springer, 2020, pp. 121–133. https...

    2020 doi

  45. [54]

    C. L. Camacho-Villal´ on, M. Dorigo, T. St¨ utzle, Why the Intelligent Wa- ter Drops Cannot Be Considered as a Novel Algorithm, in: M. Dorigo, et al. (Eds.), International Conference on Swarm Intelligence, Springer, 2018, pp. 302–314. https://doi.org/10.1007/978-3-030-00533-7_ 24

    2018 doi

  46. [55]

    C. L. Camacho-Villal´ on, M. Dorigo, T. St¨ utzle, The intelligent water drops algorithm: why it cannot be considered a novel algorithm: A brief discussion on the use of metaphors in optimization, Swarm Intell. 13 (2019) 173–192. https://doi.org/10.1007/s11721-019-00165-y

    2019 doi

  47. [56]

    C. L. Camacho-Villal´ on, M. Dorigo, T. St¨ utzle, An analysis of why cuckoo search does not bring any novel ideas to optimization, Comput. 35 & Oper. Res. 142 (2022) 105747. https://doi.org/10.1016/j.cor. 2022.105747

    2022

  48. [57]

    C. L. Camacho-Villal´ on, M. Dorigo, T. St¨ utzle, Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors, Int. Trans. Oper. Res. 30 (2023) 2945–2971. https://doi.org/10.1111/itor.13176

    2023 doi

  49. [58]

    Castelli, L

    M. Castelli, L. Manzoni, L. Mariot, M. S. Nobile, A. Tangherloni, Salp swarm optimization: A critical review, Expert Syst. with Appl. 189 (2022) 116029. https://doi.org/10.1016/j.eswa.2021.116029

    2022

  50. [59]

    L. Deng, S. Liu, Deficiencies of the whale optimization algorithm and its validation method, Expert Syst. with Appl. 237 (2024) 121544. https: //doi.org/10.1016/j.eswa.2023.121544

    2024

  51. [60]

    Tzanetos, Does the Field of Nature-Inspired Computing Contribute to Achieving Lifelike Features?, Artif

    A. Tzanetos, Does the Field of Nature-Inspired Computing Contribute to Achieving Lifelike Features?, Artif. Life 29 (2023) 487–511. https: //doi.org/10.1162/artl_a_00407

    2023 doi

  52. [61]

    Kudela, The Evolutionary Computation Methods No One Should Use,

    J. Kudela, The Evolutionary Computation Methods No One Should Use,

  53. [62]

    Urban, A

    C. Urban, A. Min´ e, A Review of Formal Methods applied to Machine Learning, 2021. doi: 10.48550/arXiv.2104.02466. arXiv:2104.02466

    2021 doi arXiv

  54. [63]

    Swan, et al., A research agenda for metaheuristic standardization, in: MIC 2015: the XI Metaheuristics International Conference, 2015

    J. Swan, et al., A research agenda for metaheuristic standardization, in: MIC 2015: the XI Metaheuristics International Conference, 2015

    2015

  55. [64]

    In the Large

    J. Swan, S. Adriaensen, A. E. Brownlee, K. Hammond, C. G. Johnson, A. Kheiri, F. Krawiec, J. Merelo, L. L. Minku, E. ozcan, G. L. Pappa, P. Garc´ ıa-Sanchez, K. Sorensen, S. Vob, M. Wagner, D. R. White, Meta- heuristics “In the Large”’, Eur. J. of Oper. Res. 297 (2022) 393–406...

    2022 doi

  56. [65]

    Del Ser, E

    J. Del Ser, E. Osaba, A. D. Martinez, M. N. Bilbao, J. Poyatos, D. Molina, F. Herrera, 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 Com- putational Intell...

    2021

  57. [66]

    Derrac, S

    J. Derrac, S. Garc´ ıa, D. Molina, F. Herrera, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm and Evol. Com- put. 1 (2011) 3–18.https://doi.org/10.1016/j.swevo.2011.02.002

    2011 doi

  58. [67]

    Carrasco, S

    J. Carrasco, S. Garc´ ıa, M. Rueda, S. Das, F. Herrera, Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review, Swarm and Evol. Comput. 54 (2020) 100665. https://doi.org/10.1016/j. swe...

    2020

  59. [68]

    Sharma, S

    P. Sharma, S. Raju, Metaheuristic optimization algorithms: A comprehensive overview and classification of benchmark test func- tions, Soft. Comput. 28 (2024) 3123–3186. https://doi.org/10.1007/ s00500-023-09276-5

    2024

  60. [69]

    Walden, M

    A. Walden, M. Buzdalov, A Simple Statistical Test Against Origin- Biased Metaheuristics, in: S. Smith, J. Correia, C. Cintrano (Eds.), International Conference on the Applications of Evolutionary Compu- tation, Springer Nature Switzerland, 2024, pp. 322–337. https://doi. org/1...

    2024 doi

  61. [70]

    Kumar, G

    A. Kumar, G. Wu, M. Z. Ali, R. Mallipeddi, P. N. Suganthan, S. Das, A test-suite of non-convex constrained optimization problems from the real-world and some baseline results, Swarm and Evol. Comput. 56 (2020) 100693. doi:https://doi.org/10.1016/j.swevo.2020.100693

    2020

  62. [71]

    Fister, I

    I. Fister, I. Fister, A. Iglesias, A. Galvez, On detecting the novel- ties in metaphor-based algorithms, in: F. Chicano (Ed.), Proceed- ings of the Genetic and Evolutionary Computation Conference Com- panion, Association for Computing Machinery, 2021, p. 71–72. https: //doi.or...

    2021

  63. [72]

    de Armas, E

    J. de Armas, E. Lalla-Ruiz, S. L. Tilahun, S. Voß, Similarity in metaheuristics: A gentle step towards a comparison methodol- ogy, Nat. Comput. 21 (2022) 265–287. https://doi.org/10.1007/ s11047-020-09837-9

    2022

  64. [73]

    St¨ utzle, M

    T. St¨ utzle, M. L´ opez-Ib´ a˜ nez, Automated design of metaheuristic al- gorithms, Handbook of metaheuristics (2019) 541–579. https://doi. org/10.1007/978-3-319-91086-4_17 . 37

    2019 doi

  65. [74]

    Q. Zhao, Q. Duan, B. Yan, S. Cheng, Y. Shi, Automated design of metaheuristic algorithms: A survey, arXiv preprint arXiv:2303.06532 (2023). https://doi.org/10.48550/arXiv.2303.06532

    2023 doi arXiv

  66. [75]

    K. P. Choi, E. H. H. Kam, X. T. Tong, W. K. Wong, Appro- priate noise addition to metaheuristic algorithms can enhance their performance, Sci. Rep. 13 (2023) 5291. https://doi.org/10.1038/ s41598-023-29618-5

    2023

  67. [76]

    H. Jia, C. Lu, Guided learning strategy: A novel update mechanism for metaheuristic algorithms design and improvement, Knowledge-Based Syst. 286 (2024) 111402. https://doi.org/10.1016/j.knosys.2024. 111402

    2024 doi

  68. [77]

    Acosta-Ugalde, J

    D. Acosta-Ugalde, J. M. Cruz-Duarte, S. E. Conant-Pablos, J. G. Falcon-Cardona, Beyond “novel”’ metaphor-based metaheuristics: An interactive algorithm design software, in: 2024 IEEE Congress on Evo- lutionary Computation (CEC), 2024, pp. 1–8. https://doi.org/10. 1109/CEC60901...

    2024 arXiv

  69. [78]

    Mikolov, I

    T. Mikolov, I. Sutskever, K. Chen, G. Corrado, J. Dean, Distributed Representations of Words and Phrases and Their Compositionality, in: Advances in Neural Information Processing Systems, volume 26, 2013, p. 3111–3119

    2013

  70. [79]

    van Stein, T

    N. van Stein, T. B¨ ack, LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics, 2024. https: //arxiv.org/abs/2405.20132

    2024 arXiv

  71. [80]

    Romera-Paredes, M

    B. Romera-Paredes, M. Barekatain, A. Novikov, M. Balog, M. P. Ku- mar, E. Dupont, F. J. Ruiz, J. S. Ellenberg, P. Wang, O. Fawzi, et al., Mathematical discoveries from program search with large lan- guage models, Nature 625 (2024) 468–475. https://doi.org/10.1038/ s41586-023-06924-6

    2024

  72. [81]

    F. Liu, X. Tong, M. Yuan, X. Lin, F. Luo, Z. Wang, Z. Lu, Q. Zhang, An example of evolutionary computation + large language model beating human: Design of efficient guided local search, arXiv preprint arXiv:2401.02051 (2024). https://doi.org/10.48550/arXiv. 2401.02051. 38

    2024 doi arXiv

  73. [1862]

    https://doi.org/10.1007/s10462-020-09893-8

    doi

  74. [2023]

    https://arxiv.org/abs/2301.01984

    arXiv

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