Niching grey wolf optimizer for multimodal optimization problems
Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving mult...
| Main Authors: | Ahmed, R., Nazir, A., Mahadzir, S., Shorfuzzaman, M., Islam, J. |
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| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.23869 / |
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MDPI AG
2021
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107369096&doi=10.3390%2fapp11114795&partnerID=40&md5=e9c410a307716b65ed332456c2c73441 http://eprints.utp.edu.my/23869/ |
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utp-eprints.238692021-08-19T13:25:05Z Niching grey wolf optimizer for multimodal optimization problems Ahmed, R. Nazir, A. Mahadzir, S. Shorfuzzaman, M. Islam, J. Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorithm that requires less parameter tuning. However, the GWO suffers from premature convergence and fails to maintain the balance between exploration and exploitation for solving multi-modal problems. This study proposes a niching GWO (NGWO) that incorporates personal best features of PSO and a local search technique to address these issues. The proposed algorithm has been tested for 23 benchmark functions and three engineering cases. The NGWO outperformed all other considered algorithms in most of the test functions compared to state-of-the-art metaheuristics such as PSO, GSA, GWO, Jaya and two improved variants of GWO, and niching CSA. Statistical analysis and Friedman tests have been conducted to compare the performance of these algorithms thoroughly. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. MDPI AG 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107369096&doi=10.3390%2fapp11114795&partnerID=40&md5=e9c410a307716b65ed332456c2c73441 Ahmed, R. and Nazir, A. and Mahadzir, S. and Shorfuzzaman, M. and Islam, J. (2021) Niching grey wolf optimizer for multimodal optimization problems. Applied Sciences (Switzerland), 11 (11). http://eprints.utp.edu.my/23869/ |
| institution |
Universiti Teknologi Petronas |
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UTP Institutional Repository |
| description |
Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorithm that requires less parameter tuning. However, the GWO suffers from premature convergence and fails to maintain the balance between exploration and exploitation for solving multi-modal problems. This study proposes a niching GWO (NGWO) that incorporates personal best features of PSO and a local search technique to address these issues. The proposed algorithm has been tested for 23 benchmark functions and three engineering cases. The NGWO outperformed all other considered algorithms in most of the test functions compared to state-of-the-art metaheuristics such as PSO, GSA, GWO, Jaya and two improved variants of GWO, and niching CSA. Statistical analysis and Friedman tests have been conducted to compare the performance of these algorithms thoroughly. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
| format |
Article |
| author |
Ahmed, R. Nazir, A. Mahadzir, S. Shorfuzzaman, M. Islam, J. |
| spellingShingle |
Ahmed, R. Nazir, A. Mahadzir, S. Shorfuzzaman, M. Islam, J. Niching grey wolf optimizer for multimodal optimization problems |
| author_sort |
Ahmed, R. |
| title |
Niching grey wolf optimizer for multimodal optimization problems |
| title_short |
Niching grey wolf optimizer for multimodal optimization problems |
| title_full |
Niching grey wolf optimizer for multimodal optimization problems |
| title_fullStr |
Niching grey wolf optimizer for multimodal optimization problems |
| title_full_unstemmed |
Niching grey wolf optimizer for multimodal optimization problems |
| title_sort |
niching grey wolf optimizer for multimodal optimization problems |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107369096&doi=10.3390%2fapp11114795&partnerID=40&md5=e9c410a307716b65ed332456c2c73441 http://eprints.utp.edu.my/23869/ |
| _version_ |
1741196745788882944 |
| score |
11.62408 |