Deduplicated Budget-Matched Benchmarking of Metaheuristic Feature Selection for PMSM Inverter Fault Diagnosis

Metaheuristic feature-selection studies can overstate optimizer-specific effects when data duplicates, unequal evaluation budgets, weak baselines and representation choices are not controlled. This study therefore presents a deduplicated, budget-matched benchmark of six recent metaheuristics for permanent magnet synchronous motor (PMSM) inverter fault diagnosis rather than proposing a new optimizer or deployable diagnostic system. After removing 17 surplus exact-duplicate rows, the primary analysis uses 10,875 unique observations, class-wise blocked nested validation and an approximately 300-unique-evaluation budget, together with genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), random search, deterministic greedy selection and classifier-representation controls. The Runge–Kutta optimizer (RUN) achieved a mean macro-averaged F1 score (macro-F1) = 0.9473 with 4.20 features, while greedy forward selection achieved 0.9476 with 5.00 features; their paired fold difference was negligible (exact sign-flip p = 0.9375), and the six recent optimizers did not differ significantly at fold level (Friedman p = 0.2428). The evidence therefore supports sparse feature search but not metaheuristic superiority. Because each fault class originates from one approximately 10 Hz recording, the conclusions are restricted to blocked within-session benchmarking and do not establish cross-session, high-frequency or deployment reliability.