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This study introduces three key contributions to address this challenge: (1) a novel systematic taxonomic framework that employs nineteen rigorously selected, metaphor-free criteria to evaluate algorithmic distinctiveness; (2) a comprehensive clustering methodology that combines Rogers-Tanimoto distance analysis with principal component analysis (PCA) and hierarchical clustering to quantify algorithmic similarities; and (3) an objective assessment method for evaluating genuine algorithmic innovations. Through the analysis of 145 metaheuristic algorithms, we demonstrate that 74 algorithms (51.0%) exhibit distances below the confidence interval threshold, indicating profound structural overlap. Network analysis reveals 26 algorithms with perfect structural identity (distance\u2009=\u20090.0) and 512 algorithm pairs showing high similarity (distance\u2009&lt;\u20090.039), representing 18.9% of all pairwise comparisons. The results show that numerous algorithms claiming innovation deliver only incremental modifications to existing implementation patterns, lacking fundamental methodological advancement. The framework provides both a theoretical foundation for understanding algorithmic similarities and a practical tool for evaluating new algorithmic proposals, potentially transforming how the field assesses and develops novel optimization methods.<\/jats:p>","DOI":"10.1007\/s10462-025-11456-8","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T09:03:59Z","timestamp":1766480639000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Systematic taxonomic framework of metaheuristic algorithms using hierarchical clustering and structural criteria: how novel is the novelty?"],"prefix":"10.1007","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4312-6300","authenticated-orcid":false,"given":"Manuel","family":"Soto Calvo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7749-0317","authenticated-orcid":false,"given":"Han Soo","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,23]]},"reference":[{"key":"11456_CR1","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/B978-0-12-813314-9.00010-4","volume-title":"Computational intelligence for multimedia big data on the cloud with engineering applications","author":"M Abdel-Basset","year":"2018","unstructured":"Abdel-Basset M, Abdel-Fatah L, Sangaiah AK (2018) Metaheuristic algorithms: a comprehensive review. 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