PID Parameter Optimization Based on an Improved Educational Competition Optimization Algorithm

The Educational Competition Optimization (ECO) algorithm is a novel intelligent optimization method that simulates the competitive mechanisms within an educational system and employs a cyclic search framework to balance exploration and exploitation, demonstrating superior performance in benchmark functions. However, ECO suffers from the drawbacks of easily falling into local optima and exhibiting slow convergence in PID parameter tuning. To enhance its applicability in PID parameter optimization, this study proposes an Improved Educational Competition Optimization (IECO) algorithm. The proposed enhancements include utilizing sine mapping to generate the initial population, thereby increasing population diversity and expanding the solution space, as well as introducing Cauchy distribution perturbation to replace the conventional iterative update mechanism, significantly improving global search capability. To validate the effectiveness of these improvements, comparative experiments were conducted between IECO and conventional ECO, Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). Simulation results indicate that the PID controller optimized by IECO exhibits significant advantages, including shorter settling time, smaller overshoot, and faster convergence speed.