aspg-31-4456-1791686946 20261011024906 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 2026 8 2 Amer Ramadan Faculty of Electrical Engineering, University of Belgrade, Serbia Ana Jurasovi´c Faculty of Agriculture Engineering, University of Belgrade, Serbia Metaheuristic optimization is extensively used in electrical and industrial engineering, where nonlinear, nonconvex, mixed-variable, and simulation-based models arise in power-system operation, energy management, production planning, scheduling, and manufacturing-system design. The rapid proliferation of newly named optimizers, however, makes it difficult to determine whether an apparent contribution introduces a distinct search mechanism or repackages an established algorithmic lineage. This article presents Metaheuristic Lineage Mining (MLM), a multimodal screening framework that integrates executable equation structure, normalized source-code structure, and empirical search dynamics. Identifier-invariant abstract-syntax motifs represent update equations, normalized token n-grams characterize implementation structure, and trajectory fingerprints summarize convergence, diversity, exploration, and improvement behavior. The evaluation includes 57 implementations grouped into 16 documented variant families and 1,710 controlled runs over six continuous benchmark functions and five random seeds. Fusion weights are selected under lineage-held-out validation, preventing algorithms from the evaluated family from influencing model selection. Code similarity is the strongest individual channel, with a mean ROC–AUC of 0.955 and top-1 lineage retrieval of 0.930; equation similarity reaches a ROC–AUC of 0.929. Search-dynamics similarity is weaker alone but supplies behaviorally independent corroboration. Constrained multimodal fusion yields the highest average precision of 0.769, a mean reciprocal rank of 0.935, and clustering normalized mutual information of 0.885. Interpretable cross-family associations, including WhaleFOA–WOA and JADE–SHADE, demonstrate the ability to expose hybrid or ancestral relations that categorical labels omit. The framework is not a detector of misconduct; it is an auditable engineering due-diligence tool for evaluating optimizer originality before costly application studies in power, energy, manufacturing, logistics, and production systems. 2026 01 10 4456 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAACI.080201 https://www.americaspg.com/journal/31/article/4456 https://www.americaspg.com/storage/articles/manuscripts/01KY5V7K72N19RMP92P4YR3MV4.pdf