Open Access

ARTICLE

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

Khizer Mehmood¹, Naveed Ishtiaq Chaudhary^2,*, Zeshan Aslam Khan³, Khalid Mehmood Cheema⁴, Muhammad Asif Zahoor Raja², Sultan S. Alshamrani⁵, Kaled M. Alshmrany⁶

1 Department of Electrical and Computer Engineering, International Islamic University, Islamabad, 44000, Pakistan
2 Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan
3 International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan
4 Department of Electronic Engineering, Fatima Jinnah Women University, Rawalpindi, 46000, Pakistan
5 Department of Information Technology, College of Computer and Information Technology, Taif University, Taif, 21944, Saudi Arabia
6 Institute of Public Administration, Jeddah, 21944, Saudi Arabia

* Corresponding Author: Naveed Ishtiaq Chaudhary. Email:

(This article belongs to the Special Issue: Advances in Swarm Intelligence Algorithms)

Computer Modeling in Engineering & Sciences 2025, 143(2), 1809-1841. https://doi.org/10.32604/cmes.2025.064900

Received 27 February 2025; Accepted 23 April 2025; Issue published 30 May 2025

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.

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Keywords

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