Volume 8 • Issue 2 • PP: 01–10 • 2026
Metaheuristic Lineage Mining for Electrical and Industrial Engineering Optimization: Detecting Rebranded Algorithms through Equation, Code, and Search-Dynamics Similarity
Open Access & Copyright
© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
View PDF Abstract
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.
Keywords
References
[1] G. H. Valencia-Rivera, M. T. Benavides-Robles, A. Vela Morales, I. Amaya, J. M. Cruz-Duarte, J. C. Ortiz-Bayliss, and J. G. Avina-Cervantes, “A systematic review of metaheuristic algorithms in electric power systems optimization,” Applied Soft Computing, vol. 150, p. 111047, 2024.
[2] M. Benfer, V. Heyer, O. Brützel, C. Liebrecht, S. Peukert, and G. Lanza, “Analysis of metaheuristic optimisation techniques for simulated matrix production systems,” Production Engineering, vol. 18, pp. 159–168, 2024.
[3] Z. Ma, G. Wu, P. N. Suganthan, A. Song, and Q. Luo, “Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms,” Swarm and Evolutionary Computation, vol. 77, p. 101248, 2023.
[4] M. A. Lones, “Mitigating metaphors: A comprehensible guide to recent nature-inspired algorithms,” SN Computer Science, vol. 1, p. 49, 2020.
[5] C. Aranha, C. L. Camacho Villalón, F. Campelo, M. Dorigo, R. Ruiz, M. Sevaux, K. Sörensen, and T. Stützle, “Metaphor-based metaheuristics, a call for action: The elephant in the room,” Swarm Intelligence, vol. 16, no. 1, pp. 1–6, 2022.
[6] C. L. Camacho-Villalón, M. Dorigo, and T. Stützle, “Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms: Six misleading optimization techniques inspired by bestial metaphors,” International Transactions in Operational Research, vol. 30, no. 6, pp. 2945–2971, 2023.
[7] L. Velasco, H. Guerrero, and A. Hospitaler, “A literature review and critical analysis of metaheuristics recently developed,” Archives of Computational Methods in Engineering, vol. 31, pp. 125–146, 2024.
[8] H. Stegherr, M. Heider, and J. Hähner, “Classifying metaheuristics: Towards a unified multi-level classification system,” Natural Computing, vol. 21, pp. 155–171, 2022.
[9] J. Stork, A. E. Eiben, and T. Bartz-Beielstein, “A new taxonomy of global optimization algorithms,” Natural Computing, vol. 21, no. 2, pp. 219–242, 2022.
[10] J. de Armas, E. Lalla-Ruiz, S. L. Tilahun, and S. Voß, “Similarity in metaheuristics: A gentle step towards a comparison methodology,” Natural Computing, vol. 21, pp. 265–287, 2022.
[11] Z. Hu, Q. Zhang, Y. Wang, Q. Su, and Z. Xiong, “Research orientation and novelty discriminant for new metaheuristic algorithms,” Applied Soft Computing, vol. 157, p. 111521, 2024.
[12] A. E. Ezugwu, A. K. Shukla, R. Nath, A. A. Akinyelu, J. O. Agushaka, H. Chiroma, and P. K. Muhuri, “Metaheuristics: A comprehensive overview and classification along with bibliometric analysis,” Artificial Intelligence Review, vol. 54, pp. 4237–4316, 2021.
[13] T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh, and S. Mirjalili, “Particle swarm optimization: A comprehensive survey,” IEEE Access, vol. 10, pp. 10 031–10 061, 2022.
[14] L. Hayward and A. P. Engelbrecht, “Determining metaheuristic similarity using behavioral analysis,” IEEE Transactions on Evolutionary Computation, vol. 29, no. 1, pp. 262–274, 2025.
[15] M. Zakeri-Nasrabadi, S. Parsa, M. Ramezani, C. K. Roy, and M. Ekhtiarzadeh, “A systematic literature review on source code similarity measurement and clone detection: Techniques, applications, and challenges,” Journal of Systems and Software, vol. 204, p. 111796, 2023.
[16] N. Van Thieu and S. Mirjalili, “MEALPY: An opensource library for latest meta-heuristic algorithms in python,” Journal of Systems Architecture, vol. 139, p. 102871, 2023.
[17] J. Blank and K. Deb, “pymoo: Multi-objective optimization in python,” IEEE Access, vol. 8, pp. 89 497–89 509, 2020.
[18] A. Ramírez, R. Barbudo, and J. R. Romero, “An experimental comparison of metaheuristic frameworks for multi-objective optimization,” Expert Systems, vol. 40, no. 4, p. e12672, 2023.
[19] E. Osaba, E. Villar-Rodriguez, J. Del Ser, A. J. Nebro, D. Molina, A. LaTorre, P. N. Suganthan, C. A. Coello Coello, and F. Herrera, “A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems,” Swarm and Evolutionary Computation, vol. 64, p. 100888, 2021.
[20] N. Hansen, A. Auger, R. Ros, O. Mersmann, T. Tušar, and D. Brockhoff, “COCO: A platform for comparing continuous optimizers in a black-box setting,” Optimization Methods and Software, vol. 36, no. 1, pp. 114–144, 2021.
[21] F. Campelo and E. F.Wanner, “Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances,” Journal of Heuristics, vol. 26, pp. 851–883, 2020.
[22] M. Ravber, S.-H. Liu, M. Mernik, and M. ˇ Crepinšek, “Maximum number of generations as a stopping criterion considered harmful,” Applied Soft Computing, vol. 128, p. 109478, 2022.
[23] K. Rajwar and K. Deep, “Structural bias in metaheuristic algorithms: Insights, open problems, and future prospects,” Swarm and Evolutionary Computation, vol. 92, p. 101812, 2025.
[24] N. Van Thieu, “MEALPY version 3.0.1,” Zenodo software archive, 2023, version 3.0.1.
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.