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<p>The proliferation of metaheuristic optimization algorithms has led to concerns about their novelty. 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 = 0.0) and 512 algorithm pairs showing high similarity (distance &lt; 0.039), 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.</p>

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Systematic taxonomic framework of metaheuristic algorithms using hierarchical clustering and structural criteria: how novel is the novelty?

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月23日

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  • Manuel Soto Calvo,
  • Han Soo Lee

摘要

The proliferation of metaheuristic optimization algorithms has led to concerns about their novelty. 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 = 0.0) and 512 algorithm pairs showing high similarity (distance < 0.039), 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.