Grey Wolf Optimizer for Solving Economic Dispatch Problems
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) which inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for sim...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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eSAT Publishing House Pvt. Ltd.
2014
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| Online Access: | http://umpir.ump.edu.my/id/eprint/7539/ http://umpir.ump.edu.my/id/eprint/7539/1/fkee-2014-wong-Grey%20Wolf%20Optimizer-conf.pdf http://umpir.ump.edu.my/id/eprint/7539/2/fkee-2014-wong-Grey%20Wolf%20Optimizer-conf-pg1.pdf |
| _version_ | 1848818023406764032 |
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| author | Wong, Lo Ing M. H., Sulaiman Mohd Rusllim, Mohamed Hong, Mee Song |
| author_facet | Wong, Lo Ing M. H., Sulaiman Mohd Rusllim, Mohamed Hong, Mee Song |
| author_sort | Wong, Lo Ing |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) which inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 20 generating units in economic dispatch, and the results are verified by a comparative study with Biogeography-based optimization (BBO), Lambda Iteration method (LI), Hopfield model based approach (HM), Cuckoo Search (CS), Firefly, Artificial Bee Colony (ABC), Neural Networks training by Artificial Bee Colony (ABCNN), Quadratic Programming (QP) and General Algebraic Modeling System (GAMS). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. |
| first_indexed | 2025-11-15T01:31:04Z |
| format | Conference or Workshop Item |
| id | ump-7539 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T01:31:04Z |
| publishDate | 2014 |
| publisher | eSAT Publishing House Pvt. Ltd. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-75392018-04-11T03:15:43Z http://umpir.ump.edu.my/id/eprint/7539/ Grey Wolf Optimizer for Solving Economic Dispatch Problems Wong, Lo Ing M. H., Sulaiman Mohd Rusllim, Mohamed Hong, Mee Song TK Electrical engineering. Electronics Nuclear engineering This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) which inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 20 generating units in economic dispatch, and the results are verified by a comparative study with Biogeography-based optimization (BBO), Lambda Iteration method (LI), Hopfield model based approach (HM), Cuckoo Search (CS), Firefly, Artificial Bee Colony (ABC), Neural Networks training by Artificial Bee Colony (ABCNN), Quadratic Programming (QP) and General Algebraic Modeling System (GAMS). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. eSAT Publishing House Pvt. Ltd. 2014 Conference or Workshop Item NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/7539/1/fkee-2014-wong-Grey%20Wolf%20Optimizer-conf.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/7539/2/fkee-2014-wong-Grey%20Wolf%20Optimizer-conf-pg1.pdf Wong, Lo Ing and M. H., Sulaiman and Mohd Rusllim, Mohamed and Hong, Mee Song (2014) Grey Wolf Optimizer for Solving Economic Dispatch Problems. In: IEEE International Conference on Power and Energy (PECON 2014) , 1-3 December 2014 , Kuching, Sarawak. pp. 1-5., 3 (2). ISSN 2321-7308 (print); 2319-1163 (online) (Unpublished) (Unpublished) |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Wong, Lo Ing M. H., Sulaiman Mohd Rusllim, Mohamed Hong, Mee Song Grey Wolf Optimizer for Solving Economic Dispatch Problems |
| title | Grey Wolf Optimizer for Solving Economic Dispatch Problems |
| title_full | Grey Wolf Optimizer for Solving Economic Dispatch Problems |
| title_fullStr | Grey Wolf Optimizer for Solving Economic Dispatch Problems |
| title_full_unstemmed | Grey Wolf Optimizer for Solving Economic Dispatch Problems |
| title_short | Grey Wolf Optimizer for Solving Economic Dispatch Problems |
| title_sort | grey wolf optimizer for solving economic dispatch problems |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/7539/ http://umpir.ump.edu.my/id/eprint/7539/1/fkee-2014-wong-Grey%20Wolf%20Optimizer-conf.pdf http://umpir.ump.edu.my/id/eprint/7539/2/fkee-2014-wong-Grey%20Wolf%20Optimizer-conf-pg1.pdf |