Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey
Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics al...
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33580/1/Metaheuristics%20algorithms%20to%20identify%20nonlinear%20hammerstein%20model_a%20decade%20survey.pdf |
| _version_ | 1825814234607386624 |
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| author | Jui, Julakha Jahan Mohd Ashraf, Ahmad Muhammad Ikram, Mohd Rashid |
| author_facet | Jui, Julakha Jahan Mohd Ashraf, Ahmad Muhammad Ikram, Mohd Rashid |
| author_sort | Jui, Julakha Jahan |
| collection | UMP |
| description | Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area. |
| first_indexed | 2024-03-06T12:55:46Z |
| format | Article |
| id | UMPir33580 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:55:46Z |
| publishDate | 2022 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | UMPir335802022-04-15T07:21:48Z http://umpir.ump.edu.my/id/eprint/33580/ Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey Jui, Julakha Jahan Mohd Ashraf, Ahmad Muhammad Ikram, Mohd Rashid T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area. Institute of Advanced Engineering and Science 2022-02 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/33580/1/Metaheuristics%20algorithms%20to%20identify%20nonlinear%20hammerstein%20model_a%20decade%20survey.pdf Jui, Julakha Jahan and Mohd Ashraf, Ahmad and Muhammad Ikram, Mohd Rashid (2022) Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey. Bulletin of Electrical Engineering and Informatics, 11 (1). pp. 454-465. ISSN 2089-3191. (Published) https://doi.org/10.11591/eei.v11i1.3296 Publisher https://doi.org/10.11591/eei.v11i1.3296 Publisher |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Jui, Julakha Jahan Mohd Ashraf, Ahmad Muhammad Ikram, Mohd Rashid Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey |
| title | Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey |
| title_full | Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey |
| title_fullStr | Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey |
| title_full_unstemmed | Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey |
| title_short | Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey |
| title_sort | metaheuristics algorithms to identify nonlinear hammerstein model a decade survey |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/33580/1/Metaheuristics%20algorithms%20to%20identify%20nonlinear%20hammerstein%20model_a%20decade%20survey.pdf |
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