Parameter estimation of fractional-order system with improved Archimedes optimization algorithm

Yinbin Chen (), Renhuan Yang, Xiuzeng Yang, Renyu Yang, Qidong Huang, Guilian Chen, Ling Zhang, Mengyu Wei and Yongqiang Zhou
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Yinbin Chen: College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China
Renhuan Yang: College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China
Xiuzeng Yang: ��Department of Physics and Electronic Engineering, Guangxi Normal University for Nationalities, Chongzuo 532200, P. R. China
Renyu Yang: ��The School of Information Science, Guangdong University of Finance Economics, Guangzhou 510320, P. R. China
Qidong Huang: College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China
Guilian Chen: College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China
Ling Zhang: �Experiment and Training Center, Guangzhou Vocational College of Technology & Business, Guangzhou 510632, P. R. China
Mengyu Wei: �Faculty of Science and Technology, University of Macau, Macau 999078, P. R. China
Yongqiang Zhou: ��School of Electronics and Information Engineering, Wuyi University, Jiangmen 529020, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 03, 1-13

Abstract: In this paper, aiming at the problems of slow estimation speed and low estimation precision of traditional fractional-order system (FOS) parameter estimation method, an improved Archimedes optimization algorithm (IAOA) is proposed to calculate the optimal value. By establishing the parameter estimation model and the cost function, the parameter estimation problem is formulated as an optimization problem. As opposed to the Archimedes optimization algorithm (AOA), the IAOA introduces three improvements: leadership behavior, levy flight behavior and a new adaptive strategy. This paper verifies the performance of the IAOA by selecting 10 classic test functions. IAOA is applied to the parameter estimation problem of fractional-order unified system to verify the accuracy and feasibility of the algorithm. The simulation results prove that the IAOA has better global optimization ability and estimation accuracy than the original algorithm.

Keywords: Parameter estimation; fractional-order system; improved Archimedes optimization algorithm; intelligent optimization algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124501973

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