Adaptive IIR model identification using chaotic opposition-based whale optimization algorithm
Abstract Infinite impulse response (IIR) filter system recognition is a serious issue nowadays as it has many applications on a diversity of platforms. The whale optimization algorithm (WOA) is a novel nature-motivated population-based meta-heuristic algorithm where the hunting techniques of humpbac...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-07-01
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| Series: | Journal of Electrical Systems and Information Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43067-023-00102-4 |
| _version_ | 1797778964847001600 |
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| author | Souvik Dey Provas Kumar Roy Angsuman Sarkar |
| author_facet | Souvik Dey Provas Kumar Roy Angsuman Sarkar |
| author_sort | Souvik Dey |
| collection | DOAJ |
| description | Abstract Infinite impulse response (IIR) filter system recognition is a serious issue nowadays as it has many applications on a diversity of platforms. The whale optimization algorithm (WOA) is a novel nature-motivated population-based meta-heuristic algorithm where the hunting techniques of humpback whales are implemented to solve many optimization problems. But the main disadvantage of WOA is its stagnant convergence rate. As the algorithm is population based, the initialization process is very important in finding the best result and to enhance the convergence rate. In this paper, a novel chaotic oppositional-based initialization process is nominated before the start of conventional WOA to improve the performance. To effectively cover the entire search region, a chaotic-based logistic population map consists of both the actual numbers and its corresponding opposite numbers are incorporated into this opposition-based initialization process. When checked out with some classic model of examples, simulation performance authorizes chaotic oppositional-based whale optimization algorithm (COWOA) as a more convenient contender compared to the other evolutionary techniques in terms of accuracy and convergence speed. Convergence profile and mean square error are the performance specifications that are needed to inspect the performance of our recommended algorithm. |
| first_indexed | 2024-03-12T23:25:07Z |
| format | Article |
| id | doaj.art-50ffaae2532f42a59823c5e1bc2888a3 |
| institution | Directory Open Access Journal |
| issn | 2314-7172 |
| language | English |
| last_indexed | 2024-03-12T23:25:07Z |
| publishDate | 2023-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Electrical Systems and Information Technology |
| spelling | doaj.art-50ffaae2532f42a59823c5e1bc2888a32023-07-16T11:12:01ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722023-07-0110112310.1186/s43067-023-00102-4Adaptive IIR model identification using chaotic opposition-based whale optimization algorithmSouvik Dey0Provas Kumar Roy1Angsuman Sarkar2Department of Electronics and Communication Engineering, Bengal Institute of TechnologyDepartment of Electrical Engineering, Kalyani Govt. Engineering CollegeDepartment of Electronics and Communication Engineering, Kalyani Govt. Engineering CollegeAbstract Infinite impulse response (IIR) filter system recognition is a serious issue nowadays as it has many applications on a diversity of platforms. The whale optimization algorithm (WOA) is a novel nature-motivated population-based meta-heuristic algorithm where the hunting techniques of humpback whales are implemented to solve many optimization problems. But the main disadvantage of WOA is its stagnant convergence rate. As the algorithm is population based, the initialization process is very important in finding the best result and to enhance the convergence rate. In this paper, a novel chaotic oppositional-based initialization process is nominated before the start of conventional WOA to improve the performance. To effectively cover the entire search region, a chaotic-based logistic population map consists of both the actual numbers and its corresponding opposite numbers are incorporated into this opposition-based initialization process. When checked out with some classic model of examples, simulation performance authorizes chaotic oppositional-based whale optimization algorithm (COWOA) as a more convenient contender compared to the other evolutionary techniques in terms of accuracy and convergence speed. Convergence profile and mean square error are the performance specifications that are needed to inspect the performance of our recommended algorithm.https://doi.org/10.1186/s43067-023-00102-4Adaptive IIR filterMeta-heuristic algorithmWhale optimization algorithm (WOA)Chaotic oppositional-based whale optimization algorithm (COWOA)System identification |
| spellingShingle | Souvik Dey Provas Kumar Roy Angsuman Sarkar Adaptive IIR model identification using chaotic opposition-based whale optimization algorithm Journal of Electrical Systems and Information Technology Adaptive IIR filter Meta-heuristic algorithm Whale optimization algorithm (WOA) Chaotic oppositional-based whale optimization algorithm (COWOA) System identification |
| title | Adaptive IIR model identification using chaotic opposition-based whale optimization algorithm |
| title_full | Adaptive IIR model identification using chaotic opposition-based whale optimization algorithm |
| title_fullStr | Adaptive IIR model identification using chaotic opposition-based whale optimization algorithm |
| title_full_unstemmed | Adaptive IIR model identification using chaotic opposition-based whale optimization algorithm |
| title_short | Adaptive IIR model identification using chaotic opposition-based whale optimization algorithm |
| title_sort | adaptive iir model identification using chaotic opposition based whale optimization algorithm |
| topic | Adaptive IIR filter Meta-heuristic algorithm Whale optimization algorithm (WOA) Chaotic oppositional-based whale optimization algorithm (COWOA) System identification |
| url | https://doi.org/10.1186/s43067-023-00102-4 |
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