Comparative Performance Analysis of Nature-Inspired Metaheuristic Algorithms for PID Controller Optimization
TL;DR
Among the evaluated methods, PSO consistently provides the best overall performance by achieving faster convergence and superior solution quality under limited function evaluations, while GWO and WOA exhibit competitive and robust performance across challenging benchmark problems.
Abstract
Tuning PID controllers for diverse dynamic systems remains a fundamental challenge in control engineering due to the conflicting requirements of fast transient response, robustness, and stability. This study presents a comprehensive comparative analysis of eight nature-inspired metaheuristic algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Simulated Annealing (SA), Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA), Ant Lion Optimizer (ALO), and Differential Evolution (DE)—for optimal PID parameter tuning. The algorithms are evaluated using six benchmark plants representing a wide range of control characteristics, including third-order, high-order, non-minimum phase, unstable, overdamped, and underdamped systems. Optimization performance is assessed based on four integral error criteria (ISE, IAE, ITSE, and ITAE), robustness over multiple independent runs, convergence behavior, and computational efficiency under a fixed computational budget. Comparative results demonstrate that algorithm performance strongly depends on plant dynamics and optimization criteria. Among the evaluated methods, PSO consistently provides the best overall performance by achieving faster convergence and superior solution quality under limited function evaluations, while GWO and WOA exhibit competitive and robust performance across challenging benchmark problems. The findings provide practical guidelines for selecting appropriate metaheuristic optimization algorithms for PID controller design and offer useful insights into the trade-offs between optimization accuracy, robustness, and computational cost in engineering control applications.