Modified Grey Wolf Optimizer for Global Engineering Optimization
Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechani...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2016/7950348 |
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| _version_ | 1832557022959108096 |
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| author | Nitin Mittal Urvinder Singh Balwinder Singh Sohi |
| author_facet | Nitin Mittal Urvinder Singh Balwinder Singh Sohi |
| author_sort | Nitin Mittal |
| collection | DOAJ |
| description | Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer (GWO), which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO (mGWO) is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms. |
| format | Article |
| id | doaj-art-643dfc34ad2d4d67ae7bed9cc551bc8e |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-643dfc34ad2d4d67ae7bed9cc551bc8e2025-02-03T05:43:52ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322016-01-01201610.1155/2016/79503487950348Modified Grey Wolf Optimizer for Global Engineering OptimizationNitin Mittal0Urvinder Singh1Balwinder Singh Sohi2Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab 140413, IndiaDepartment of Electronics and Communication Engineering, Thapar University, Patiala, Punjab 147004, IndiaDepartment of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab 140413, IndiaNature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer (GWO), which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO (mGWO) is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms.http://dx.doi.org/10.1155/2016/7950348 |
| spellingShingle | Nitin Mittal Urvinder Singh Balwinder Singh Sohi Modified Grey Wolf Optimizer for Global Engineering Optimization Applied Computational Intelligence and Soft Computing |
| title | Modified Grey Wolf Optimizer for Global Engineering Optimization |
| title_full | Modified Grey Wolf Optimizer for Global Engineering Optimization |
| title_fullStr | Modified Grey Wolf Optimizer for Global Engineering Optimization |
| title_full_unstemmed | Modified Grey Wolf Optimizer for Global Engineering Optimization |
| title_short | Modified Grey Wolf Optimizer for Global Engineering Optimization |
| title_sort | modified grey wolf optimizer for global engineering optimization |
| url | http://dx.doi.org/10.1155/2016/7950348 |
| work_keys_str_mv | AT nitinmittal modifiedgreywolfoptimizerforglobalengineeringoptimization AT urvindersingh modifiedgreywolfoptimizerforglobalengineeringoptimization AT balwindersinghsohi modifiedgreywolfoptimizerforglobalengineeringoptimization |