Comparison of Population-Based Metaheuristics for Switching Angle Optimization in a Seven-Level Cascaded H-Bridge Inverter

Differential Evolution (DE), Teaching–Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), and the Ali Baba and the Forty Thieves Algorithm (AFTA) are compared for switching angle calculation in an ideal seven-level single-phase cascaded H-bridge inverter. Each method uses 30 paired seeds, 1000 candidates, and 201,000 objective-function evaluations. The common objective combines voltage THD through the 49th harmonic with absolute fundamental voltage error relative to 250 V peak. DE and AFTA attain THD49 = 11.6691079% and meet the empirical target in all 30 runs; TLBO succeeds in 2 runs and GWO in none. DE requires a median of 29,361 evaluations to reach the target, compared with 53,822.5 for AFTA. AFTA has the lowest mean full-budget runtime, 1.2354 s, versus 2.0742 s for DE. Paired tests with multiplicity correction distinguish DE/AFTA from TLBO/GWO in final objective value, but do not distinguish DE from AFTA at the declared numerical resolution. The maximum separation between the best THD values is only 0.0004657 percentage points. The results separate numerical precision, search effort, and runtime within one analytical operating condition; they do not establish global optimality or a practically significant electrical advantage.