Comparative Perception Dynamics of Evolutionary Optimizers under Aircraft Pitch Control System

This study investigates the perception behavior of Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) during aircraft pitch control tuning under identical dynamic conditions. Each algorithm was executed through fifty independent perceptual runs to evaluate perception correlation (ρ) and parameter sensitivity (r) as quantitative measures of the various ways of thinking of an algorithm about the search space (algorithmic cognition). The mean correlation values observed were approximately ρ(DE–GA) = + 0.10, ρ(DE–PSO) = + 0.22, and ρ(GA–PSO) = + 0.18, indicating both alignment (two algorithms interpret the control surface in a similar way) and polarity shifts (perception correlation (ρ) between two algorithms changes sign) across optimizers. DE maintained consistent perception with stable sensitivity in K₃, GA showed fluctuating behavior due to high variance in parameter response, while PSO achieved balanced perception between both extremes. These results confirm that perception coherence and polarity vary systematically with algorithmic structure, providing a measurable interpretation of optimizer cognition beyond conventional convergence or performance indices.