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Identification of continuous-time hammerstein model using improved archimedes optimization algorithm

Although various optimization algorithms have been widely employed in multiple applications, the traditional Archimedes optimization algorithm (AOA) has presented imbalanced exploration with exploitation phases and the propensity for local optima entrapment. Therefore, this article identified variou...

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Main Authors: Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad, Cho, Bo Wen
Format: Article
Language:English
English
Published: KeAi Communications Co. 2024
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/42713/1/1-s2.0-S2666307424000378-main.pdf
http://umpir.ump.edu.my/id/eprint/42713/7/Identification%20of%20continuous-time%20Hammerstein%20model.pdf
http://umpir.ump.edu.my/id/eprint/42713/
https://doi.org/10.1016/j.ijcce.2024.09.004
https://doi.org/10.1016/j.ijcce.2024.09.004
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spelling my.ump.umpir.427132024-11-18T07:22:52Z http://umpir.ump.edu.my/id/eprint/42713/ Identification of continuous-time hammerstein model using improved archimedes optimization algorithm Islam, Muhammad Shafiqul Mohd Ashraf, Ahmad Cho, Bo Wen TK Electrical engineering. Electronics Nuclear engineering Although various optimization algorithms have been widely employed in multiple applications, the traditional Archimedes optimization algorithm (AOA) has presented imbalanced exploration with exploitation phases and the propensity for local optima entrapment. Therefore, this article identified various continuous-time Hammerstein models based on an improved Archimedes optimization algorithm (IAOA) to address these concerns. The proposed algorithm employed two principal modifications to mitigate these issues and enhance identification accuracy: (i) exploration and exploitation phase recalibrations using a revised density decreasing factor and (ii) local optima entrapment alleviation utilizing safe experimentation dynamics. Various advantages were observed with this proposed algorithm, including a lower number of coefficient criteria, improved accuracy in Hammerstein model identification, and diminished processing demands by reducing gain redundancy between nonlinear and linear subsystems. This proposed algorithm also discerned linear and nonlinear subsystem variables within a continuous-time Hammerstein model utilizing input and output data. The process was evaluated using a numerical example and two practical experiments [twin-rotor system (TRS) and electro-mechanical positioning system (EMPS)]. Several parameters were then analyzed, such as the convergence curve of the fitness function, frequency and time domain-related responses, variable deviation index, and Wilcoxon's rank-sum test. Consequently, the proposed algorithm reliably determined the most optimal design variables during numerical trials, demonstrating 54.74% mean fitness function and 75.34% variable deviation indices enchantments compared to the traditional AOA. Improved mean fitness function values were also revealed in the TRS (11.63%) and EMPS (69.63%) assessments, surpassing the conventional algorithm. This proposed algorithm produced solutions with superior accuracy and consistency compared to various established metaheuristic strategies, including particle swarm optimizer, grey wolf optimizer, multi-verse optimizer, AOA, and a hybrid optimizer (average multi-verse optimizer-sine-cosine algorithm). KeAi Communications Co. 2024-09-30 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42713/1/1-s2.0-S2666307424000378-main.pdf pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/42713/7/Identification%20of%20continuous-time%20Hammerstein%20model.pdf Islam, Muhammad Shafiqul and Mohd Ashraf, Ahmad and Cho, Bo Wen (2024) Identification of continuous-time hammerstein model using improved archimedes optimization algorithm. International Journal of Cognitive Computing in Engineering, 5. 475 -493. ISSN 2666-3074. (Published) https://doi.org/10.1016/j.ijcce.2024.09.004 https://doi.org/10.1016/j.ijcce.2024.09.004
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Islam, Muhammad Shafiqul
Mohd Ashraf, Ahmad
Cho, Bo Wen
Identification of continuous-time hammerstein model using improved archimedes optimization algorithm
description Although various optimization algorithms have been widely employed in multiple applications, the traditional Archimedes optimization algorithm (AOA) has presented imbalanced exploration with exploitation phases and the propensity for local optima entrapment. Therefore, this article identified various continuous-time Hammerstein models based on an improved Archimedes optimization algorithm (IAOA) to address these concerns. The proposed algorithm employed two principal modifications to mitigate these issues and enhance identification accuracy: (i) exploration and exploitation phase recalibrations using a revised density decreasing factor and (ii) local optima entrapment alleviation utilizing safe experimentation dynamics. Various advantages were observed with this proposed algorithm, including a lower number of coefficient criteria, improved accuracy in Hammerstein model identification, and diminished processing demands by reducing gain redundancy between nonlinear and linear subsystems. This proposed algorithm also discerned linear and nonlinear subsystem variables within a continuous-time Hammerstein model utilizing input and output data. The process was evaluated using a numerical example and two practical experiments [twin-rotor system (TRS) and electro-mechanical positioning system (EMPS)]. Several parameters were then analyzed, such as the convergence curve of the fitness function, frequency and time domain-related responses, variable deviation index, and Wilcoxon's rank-sum test. Consequently, the proposed algorithm reliably determined the most optimal design variables during numerical trials, demonstrating 54.74% mean fitness function and 75.34% variable deviation indices enchantments compared to the traditional AOA. Improved mean fitness function values were also revealed in the TRS (11.63%) and EMPS (69.63%) assessments, surpassing the conventional algorithm. This proposed algorithm produced solutions with superior accuracy and consistency compared to various established metaheuristic strategies, including particle swarm optimizer, grey wolf optimizer, multi-verse optimizer, AOA, and a hybrid optimizer (average multi-verse optimizer-sine-cosine algorithm).
format Article
author Islam, Muhammad Shafiqul
Mohd Ashraf, Ahmad
Cho, Bo Wen
author_facet Islam, Muhammad Shafiqul
Mohd Ashraf, Ahmad
Cho, Bo Wen
author_sort Islam, Muhammad Shafiqul
title Identification of continuous-time hammerstein model using improved archimedes optimization algorithm
title_short Identification of continuous-time hammerstein model using improved archimedes optimization algorithm
title_full Identification of continuous-time hammerstein model using improved archimedes optimization algorithm
title_fullStr Identification of continuous-time hammerstein model using improved archimedes optimization algorithm
title_full_unstemmed Identification of continuous-time hammerstein model using improved archimedes optimization algorithm
title_sort identification of continuous-time hammerstein model using improved archimedes optimization algorithm
publisher KeAi Communications Co.
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42713/1/1-s2.0-S2666307424000378-main.pdf
http://umpir.ump.edu.my/id/eprint/42713/7/Identification%20of%20continuous-time%20Hammerstein%20model.pdf
http://umpir.ump.edu.my/id/eprint/42713/
https://doi.org/10.1016/j.ijcce.2024.09.004
https://doi.org/10.1016/j.ijcce.2024.09.004
_version_ 1822924736926056448
score 13.232414

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