Enhancing Grey Wolf Optimizer With Levy Flight for Engineering Applications
Since Grey Wolf Optimizer (GWO) first introduction, it continues to be used extensively today, owing to its simplicity, easy handling, and applicability to a wide range of problems. Although there are many different GWO variants in the literature, the problem that the GWO produces early convergence...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10181244/ |
| _version_ | 1827892486427639808 |
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| author | Wu Lei Wu Jiawei Meng Zezhou |
| author_facet | Wu Lei Wu Jiawei Meng Zezhou |
| author_sort | Wu Lei |
| collection | DOAJ |
| description | Since Grey Wolf Optimizer (GWO) first introduction, it continues to be used extensively today, owing to its simplicity, easy handling, and applicability to a wide range of problems. Although there are many different GWO variants in the literature, the problem that the GWO produces early convergence and inefficient results have still continued to emerge in their variants. In order to overcome the drawbacks of the GWO, the GWO integrated together with Levy Flight (LFGWO) is proposed. In order to demonstrate the overall performance of the LFGWO, experiments are conducted using the 23 standard benchmark functions and 10 composition functions of CEC 2019 compared with the other eight state-of-art algorithms. The 28 out of 33 average and 27 out of 33 standard deviation values obtained by LFGWO are all less than those obtained by the other eight optimization algorithms, which verified and demonstrated the performance, stability, and robustness of the LFGWO. The extensibility test with different scales of dimensions 50, 100, 300, and 500, is undertaken by comparing LFGWO with GWO and IGWO to assess the dimensional influence on problem consistency and optimization quality. Moreover, the performance of the LFGWO has also been tested on five real-world problems and infinite impulse response (IIR) challenging model identification, experimental results and statistical tests demonstrate that the performance of LFGWO is significantly better than the other compared algorithms, and the LFGWO is capable of solving real-world problems. |
| first_indexed | 2024-03-12T21:40:23Z |
| format | Article |
| id | doaj.art-620d4a548211499c8491241a60c386a1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T21:40:23Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-620d4a548211499c8491241a60c386a12023-07-26T23:00:50ZengIEEEIEEE Access2169-35362023-01-0111748657489710.1109/ACCESS.2023.329524210181244Enhancing Grey Wolf Optimizer With Levy Flight for Engineering ApplicationsWu Lei0https://orcid.org/0000-0001-5964-4300Wu Jiawei1Meng Zezhou2https://orcid.org/0009-0008-4001-3021Information College, North China University of Technology, Beijing, ChinaFaculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing, ChinaInformation College, North China University of Technology, Beijing, ChinaSince Grey Wolf Optimizer (GWO) first introduction, it continues to be used extensively today, owing to its simplicity, easy handling, and applicability to a wide range of problems. Although there are many different GWO variants in the literature, the problem that the GWO produces early convergence and inefficient results have still continued to emerge in their variants. In order to overcome the drawbacks of the GWO, the GWO integrated together with Levy Flight (LFGWO) is proposed. In order to demonstrate the overall performance of the LFGWO, experiments are conducted using the 23 standard benchmark functions and 10 composition functions of CEC 2019 compared with the other eight state-of-art algorithms. The 28 out of 33 average and 27 out of 33 standard deviation values obtained by LFGWO are all less than those obtained by the other eight optimization algorithms, which verified and demonstrated the performance, stability, and robustness of the LFGWO. The extensibility test with different scales of dimensions 50, 100, 300, and 500, is undertaken by comparing LFGWO with GWO and IGWO to assess the dimensional influence on problem consistency and optimization quality. Moreover, the performance of the LFGWO has also been tested on five real-world problems and infinite impulse response (IIR) challenging model identification, experimental results and statistical tests demonstrate that the performance of LFGWO is significantly better than the other compared algorithms, and the LFGWO is capable of solving real-world problems.https://ieeexplore.ieee.org/document/10181244/Benchmark functionglobal convergencegrey wolf optimizer (GWO)levy flight |
| spellingShingle | Wu Lei Wu Jiawei Meng Zezhou Enhancing Grey Wolf Optimizer With Levy Flight for Engineering Applications IEEE Access Benchmark function global convergence grey wolf optimizer (GWO) levy flight |
| title | Enhancing Grey Wolf Optimizer With Levy Flight for Engineering Applications |
| title_full | Enhancing Grey Wolf Optimizer With Levy Flight for Engineering Applications |
| title_fullStr | Enhancing Grey Wolf Optimizer With Levy Flight for Engineering Applications |
| title_full_unstemmed | Enhancing Grey Wolf Optimizer With Levy Flight for Engineering Applications |
| title_short | Enhancing Grey Wolf Optimizer With Levy Flight for Engineering Applications |
| title_sort | enhancing grey wolf optimizer with levy flight for engineering applications |
| topic | Benchmark function global convergence grey wolf optimizer (GWO) levy flight |
| url | https://ieeexplore.ieee.org/document/10181244/ |
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