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...

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Main Authors:	Wong, Lo Ing, M. H., Sulaiman, Mohd Rusllim, Mohamed, Hong, Mee Song
Format:	Conference or Workshop Item
Language:	English English
Published:	eSAT Publishing House Pvt. Ltd. 2014
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
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

id	ump-7539
recordtype	eprints
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)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
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
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.
format	Conference or Workshop Item
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
title	Grey Wolf Optimizer for Solving Economic Dispatch Problems
title_short	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_sort	grey wolf optimizer for solving economic dispatch problems
publisher	eSAT Publishing House Pvt. Ltd.
publishDate	2014
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
first_indexed	2023-09-18T22:04:14Z
last_indexed	2023-09-18T22:04:14Z
_version_	1777414601099444224

Grey Wolf Optimizer for Solving Economic Dispatch Problems

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