Design of Chaos Induced Aquila Optimizer for Parameter Estimation of Electro-Hydraulic Control System

Abstract

Aquila Optimizer (AO) is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey. AO is applied in various applications and its numerous variants were proposed in the literature. However, chaos theory has not been extensively investigated in AO. Moreover, it is still not applied in the parameter estimation of electro-hydraulic systems. In this work, ten well-defined chaotic maps were integrated into a narrowed exploitation of AO for the development of a robust chaotic optimization technique. An extensive investigation of twenty-three mathematical benchmarks and ten IEEE Congress on Evolutionary Computation (CEC) functions shows that chaotic Aquila optimization techniques perform better than the baseline technique. The investigation is further conducted on parameter estimation of an electro-hydraulic control system, which is performed on various noise levels and shows that the proposed chaotic AO with Piecewise map (CAO6) achieves the best fitness values of 2.873E−05, 1.014E−04, and 8.728E−03 at noise levels 1.300E−03, 1.300E−02, and 1.300E−01, respectively. Friedman test for repeated measures, computational analysis, and Taguchi test reflect the superiority of CAO6 against the state of the arts, demonstrating its potential for addressing various engineering optimization problems. However, the sensitivity to parameter tuning may limit its direct application to complex optimization scenarios.