Hybrid conjugate gradient methods using strong Wolfe line search for Whale Optimization Algorithm / Wan Nur Athirah Wan Mohd Zakirudin

Optimization is a mathematical model that can be found in everyday life, business, and scientific research. The aim of study is to determine the maximum and minimum of functions, which are often used in decision making. The nonlinear conjugate gradient (CG) method recently is the most used iterative...

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Main Author:	Wan Mohd Zakirudin, Wan Nur Athirah
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/97990/1/97990.pdf
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id	my-uitm-ir.97990
record_format	uketd_dc
spelling	my-uitm-ir.979902024-07-08T09:24:00Z Hybrid conjugate gradient methods using strong Wolfe line search for Whale Optimization Algorithm / Wan Nur Athirah Wan Mohd Zakirudin 2023 Wan Mohd Zakirudin, Wan Nur Athirah Algorithms Optimization is a mathematical model that can be found in everyday life, business, and scientific research. The aim of study is to determine the maximum and minimum of functions, which are often used in decision making. The nonlinear conjugate gradient (CG) method recently is the most used iterative methods for solving optimizing problems because it requires less storage and easy for implementation. The CG method can be classified into several types such as classical CG, spectral CG, and hybrid CG. The hybrid CG method is a certain combination one of the CG methods, made with the aim to improve the behavior of these methods and to avoid the jamming phenomenon. Based on the previous study, the LAMR coefficient is currently the best CG method under strong Wolfe line search. The purpose of this study is to determine the best coefficient of hybrid CG to solve unconstrained optimization test functions. Five coefficient of CG methods, PRP, FR, HS, LS and NRMI are chosen to be combined with LAMR. These methods are tested to compare their effectiveness and robustness. Based on the results, LAMR-HS achieves the highest percentage of successfully solved test problems and indicates as the best coefficient of hybrid CG method. Lastly, the implementation of LAMR-HS in the Whale Optimization Algorithm (WOA) aims to enhance the convergence speed, ultimately demonstrating the successful hybridization between the two algorithms. 2023 Thesis https://ir.uitm.edu.my/id/eprint/97990/ https://ir.uitm.edu.my/id/eprint/97990/1/97990.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Zull Pakkal, Norhaslinda
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Zull Pakkal, Norhaslinda
topic	Algorithms
spellingShingle	Algorithms Wan Mohd Zakirudin, Wan Nur Athirah Hybrid conjugate gradient methods using strong Wolfe line search for Whale Optimization Algorithm / Wan Nur Athirah Wan Mohd Zakirudin
description	Optimization is a mathematical model that can be found in everyday life, business, and scientific research. The aim of study is to determine the maximum and minimum of functions, which are often used in decision making. The nonlinear conjugate gradient (CG) method recently is the most used iterative methods for solving optimizing problems because it requires less storage and easy for implementation. The CG method can be classified into several types such as classical CG, spectral CG, and hybrid CG. The hybrid CG method is a certain combination one of the CG methods, made with the aim to improve the behavior of these methods and to avoid the jamming phenomenon. Based on the previous study, the LAMR coefficient is currently the best CG method under strong Wolfe line search. The purpose of this study is to determine the best coefficient of hybrid CG to solve unconstrained optimization test functions. Five coefficient of CG methods, PRP, FR, HS, LS and NRMI are chosen to be combined with LAMR. These methods are tested to compare their effectiveness and robustness. Based on the results, LAMR-HS achieves the highest percentage of successfully solved test problems and indicates as the best coefficient of hybrid CG method. Lastly, the implementation of LAMR-HS in the Whale Optimization Algorithm (WOA) aims to enhance the convergence speed, ultimately demonstrating the successful hybridization between the two algorithms.
format	Thesis
qualification_level	Bachelor degree
author	Wan Mohd Zakirudin, Wan Nur Athirah
author_facet	Wan Mohd Zakirudin, Wan Nur Athirah
author_sort	Wan Mohd Zakirudin, Wan Nur Athirah
title	Hybrid conjugate gradient methods using strong Wolfe line search for Whale Optimization Algorithm / Wan Nur Athirah Wan Mohd Zakirudin
title_short	Hybrid conjugate gradient methods using strong Wolfe line search for Whale Optimization Algorithm / Wan Nur Athirah Wan Mohd Zakirudin
title_full	Hybrid conjugate gradient methods using strong Wolfe line search for Whale Optimization Algorithm / Wan Nur Athirah Wan Mohd Zakirudin
title_fullStr	Hybrid conjugate gradient methods using strong Wolfe line search for Whale Optimization Algorithm / Wan Nur Athirah Wan Mohd Zakirudin
title_full_unstemmed	Hybrid conjugate gradient methods using strong Wolfe line search for Whale Optimization Algorithm / Wan Nur Athirah Wan Mohd Zakirudin
title_sort	hybrid conjugate gradient methods using strong wolfe line search for whale optimization algorithm / wan nur athirah wan mohd zakirudin
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/97990/1/97990.pdf
_version_	1804890020555784192

Hybrid conjugate gradient methods using strong Wolfe line search for Whale Optimization Algorithm / Wan Nur Athirah Wan Mohd Zakirudin

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