Metaheuristic Lineage Mining for Electrical and Industrial

Engineering Optimization: Detecting Rebranded Algorithms

through Equation, Code, and Search-Dynamics Similarity

Amer Ramadan1,* Ana Jurasovi´c2

1 Faculty of Electrical Engineering, University of Belgrade, Serbia

2 Faculty of Agriculture Engineering, University of Belgrade, Serbia

Received: March 24, 2026 R e vis ed: May 20, 2026 Accepted: July 10, 2026 ⋆ Corresponding author

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: Metaheuristic algorithms Electrical engineering optimization Industrial engineering Power systems

Production scheduling Lineage mining Code similarity Search dynamics

1. INTRODUCTION

Electrical and industrial engineering contain many optimization

problems for which exact or gradient-based methods are

difficult to apply directly. Optimal power flow, unit commitment,

microgrid energy management, production scheduling,

facility configuration, maintenance planning, and simulationbased

manufacturing design can involve nonconvex objec-