Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer

This paper presents the application of a new meta-heuristic called Grey Wolf Optimizer (GWO) which inspired by grey wolves (Canis lupus) for solving economic dispatch (ED) problems. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey w...

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Main Authors:	Lo, Ing Wong, M. H., Sulaiman, Mohd Rusllim, Mohamed
Format:	Article
Language:	English
Published:	scientific.net 2015
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/10366/ http://umpir.ump.edu.my/id/eprint/10366/ http://umpir.ump.edu.my/id/eprint/10366/ http://umpir.ump.edu.my/id/eprint/10366/1/Solving%20Economic%20Dispatch%20Problems%20with%20Practical%20Constraints%20Utilizing%20Grey%20Wolf%20Optimizer.pdf

id	ump-10366
recordtype	eprints
spelling	ump-103662018-03-07T03:48:24Z http://umpir.ump.edu.my/id/eprint/10366/ Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer Lo, Ing Wong M. H., Sulaiman Mohd Rusllim, Mohamed TK Electrical engineering. Electronics Nuclear engineering This paper presents the application of a new meta-heuristic called Grey Wolf Optimizer (GWO) which inspired by grey wolves (Canis lupus) for solving economic dispatch (ED) problems. 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. In this paper, GWO was demonstrated and tested on two well-known test systems with practical constraints. A comparison of simulation results is carried out with those published in the recent literatures. The results show that the GWO algorithm is able to provide very competitive results for nonlinear characteristics of the generators such as ramp rate limits, prohibited zone and non-smooth cost functions compared to the other well-known meta-heuristics techniques. scientific.net 2015 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/10366/1/Solving%20Economic%20Dispatch%20Problems%20with%20Practical%20Constraints%20Utilizing%20Grey%20Wolf%20Optimizer.pdf Lo, Ing Wong and M. H., Sulaiman and Mohd Rusllim, Mohamed (2015) Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer. Applied Mechanics and Materials, 785. pp. 511-515. ISSN 1662-7482 http://www.scientific.net/AMM.785.511 DOI: 10.4028/www.scientific.net/AMM.785.511
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Lo, Ing Wong M. H., Sulaiman Mohd Rusllim, Mohamed Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer
description	This paper presents the application of a new meta-heuristic called Grey Wolf Optimizer (GWO) which inspired by grey wolves (Canis lupus) for solving economic dispatch (ED) problems. 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. In this paper, GWO was demonstrated and tested on two well-known test systems with practical constraints. A comparison of simulation results is carried out with those published in the recent literatures. The results show that the GWO algorithm is able to provide very competitive results for nonlinear characteristics of the generators such as ramp rate limits, prohibited zone and non-smooth cost functions compared to the other well-known meta-heuristics techniques.
format	Article
author	Lo, Ing Wong M. H., Sulaiman Mohd Rusllim, Mohamed
author_facet	Lo, Ing Wong M. H., Sulaiman Mohd Rusllim, Mohamed
author_sort	Lo, Ing Wong
title	Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer
title_short	Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer
title_full	Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer
title_fullStr	Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer
title_full_unstemmed	Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer
title_sort	solving economic dispatch problems with practical constraints utilizing grey wolf optimizer
publisher	scientific.net
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/10366/ http://umpir.ump.edu.my/id/eprint/10366/ http://umpir.ump.edu.my/id/eprint/10366/ http://umpir.ump.edu.my/id/eprint/10366/1/Solving%20Economic%20Dispatch%20Problems%20with%20Practical%20Constraints%20Utilizing%20Grey%20Wolf%20Optimizer.pdf
first_indexed	2023-09-18T22:09:54Z
last_indexed	2023-09-18T22:09:54Z
_version_	1777414957422346240

Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer

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