Artificial Ecosystem Optimizer-Based System Identification and Its Performance Evaluation

Abstract

This study delves into the realm of system identification, a crucial sub-field in control engineering, aimed at constructing mathematical models of systems based on input/output data. This work particularly proposes the application of artificial ecosystem algorithm (AEO) for solving system identification problems. Inspired by the energy flow of natural ecosystems, AEO has undergone specific modifications leading to derived versions. Additionally, five diverse meta-heuristic algorithms are employed to assess their applicability and performance in system identification using data from an air stream heater experiment kit. A comprehensive performance comparison is made, considering time bounds, maximum generations, early stopping, and function evaluation constraints, presenting their respective performances. Among the evaluated algorithms, the AEO algorithm enhanced with the sine and cosine strategy stands out with a determined R2 value of 0.951. This algorithm consistently outperforms others in Wilcoxon tests, showcasing its significant success. Our study affirms that meta-heuristic algorithms, particularly the proposed AEO algorithm, can be effectively applied to system identification problems, yielding successful calculations of transfer function parameters.