An improved grey wolf with whale algorithm for optimization functions

The Grey Wolf Optimization (GWO) is a nature-inspired, meta-heuristic search optimization algorithm. It follows the social hierarchical structure of a wolf pack and their ability to hunt in packs. Since its inception in 2014, GWO is able to successfully solve several optimization problems and has sh...

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Main Author:	Asgher, Hafiz Maaz
Format:	Thesis
Language:	English English English
Published:	2022
Subjects:	T Technology (General)
Online Access:	http://eprints.uthm.edu.my/8263/1/24p%20HAFIZ%20MAAZ%20ASGHER.pdf http://eprints.uthm.edu.my/8263/2/HAFIZ%20MAAZ%20ASGHER%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8263/3/HAFIZ%20MAAZ%20ASGHER%20WATERMARK.pdf
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spelling	my-uthm-ep.82632023-02-07T03:42:04Z An improved grey wolf with whale algorithm for optimization functions 2022-01 Asgher, Hafiz Maaz T Technology (General) The Grey Wolf Optimization (GWO) is a nature-inspired, meta-heuristic search optimization algorithm. It follows the social hierarchical structure of a wolf pack and their ability to hunt in packs. Since its inception in 2014, GWO is able to successfully solve several optimization problems and has shown better convergence than the Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), and Evolutionary Programming (EP). Despite providing successful solutions to optimization problems, GWO has an inherent problem of poor exploration capability. The position-update equation in GWO mostly relies on the information provided by the previous solutions to generate new candidate solutions which result in poor exploration activity. Therefore, to overcome the problem of poor exploration in the GWO the exploration part of the Whale optimization algorithm (WOA) is integrated in it. The resultant Grey Wolf Whale Optimization Algorithm (GWWOA) offers better exploration ability and is able to solve the optimization problems to find the most optimal solution in search space. The performance of the proposed algorithm is tested and evaluated on five benchmarked unimodal and five multimodal functions. The simulation results show that the proposed GWWOA is able to find a fine balance between exploration and exploitation capabilities during convergence to global minima as compared to the standard GWO and WOA algorithms. 2022-01 Thesis http://eprints.uthm.edu.my/8263/ http://eprints.uthm.edu.my/8263/1/24p%20HAFIZ%20MAAZ%20ASGHER.pdf text en public http://eprints.uthm.edu.my/8263/2/HAFIZ%20MAAZ%20ASGHER%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/8263/3/HAFIZ%20MAAZ%20ASGHER%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	T Technology (General)
spellingShingle	T Technology (General) Asgher, Hafiz Maaz An improved grey wolf with whale algorithm for optimization functions
description	The Grey Wolf Optimization (GWO) is a nature-inspired, meta-heuristic search optimization algorithm. It follows the social hierarchical structure of a wolf pack and their ability to hunt in packs. Since its inception in 2014, GWO is able to successfully solve several optimization problems and has shown better convergence than the Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), and Evolutionary Programming (EP). Despite providing successful solutions to optimization problems, GWO has an inherent problem of poor exploration capability. The position-update equation in GWO mostly relies on the information provided by the previous solutions to generate new candidate solutions which result in poor exploration activity. Therefore, to overcome the problem of poor exploration in the GWO the exploration part of the Whale optimization algorithm (WOA) is integrated in it. The resultant Grey Wolf Whale Optimization Algorithm (GWWOA) offers better exploration ability and is able to solve the optimization problems to find the most optimal solution in search space. The performance of the proposed algorithm is tested and evaluated on five benchmarked unimodal and five multimodal functions. The simulation results show that the proposed GWWOA is able to find a fine balance between exploration and exploitation capabilities during convergence to global minima as compared to the standard GWO and WOA algorithms.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Asgher, Hafiz Maaz
author_facet	Asgher, Hafiz Maaz
author_sort	Asgher, Hafiz Maaz
title	An improved grey wolf with whale algorithm for optimization functions
title_short	An improved grey wolf with whale algorithm for optimization functions
title_full	An improved grey wolf with whale algorithm for optimization functions
title_fullStr	An improved grey wolf with whale algorithm for optimization functions
title_full_unstemmed	An improved grey wolf with whale algorithm for optimization functions
title_sort	improved grey wolf with whale algorithm for optimization functions
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2022
url	http://eprints.uthm.edu.my/8263/1/24p%20HAFIZ%20MAAZ%20ASGHER.pdf http://eprints.uthm.edu.my/8263/2/HAFIZ%20MAAZ%20ASGHER%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8263/3/HAFIZ%20MAAZ%20ASGHER%20WATERMARK.pdf
_version_	1776103313626890240

An improved grey wolf with whale algorithm for optimization functions

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