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Hybrid harmony search algorithm for continuous optimization problems

Harmony Search (HS) algorithm has been extensively adopted in the literature to address optimization problems in many different fields, such as industrial design, civil engineering, electrical and mechanical engineering problems. In order to ensure its search performance, HS requires extensive tunin...

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Main Author: Ala’a Atallah, Hamad Alomoush
Format: Thesis
Language:English
Published: 2020
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/33729/1/Hybrid%20harmony%20search%20algorithm%20for%20continuous.pdf
http://umpir.ump.edu.my/id/eprint/33729/
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spelling my.ump.umpir.337292022-04-13T07:29:44Z http://umpir.ump.edu.my/id/eprint/33729/ Hybrid harmony search algorithm for continuous optimization problems Ala’a Atallah, Hamad Alomoush QA76 Computer software Harmony Search (HS) algorithm has been extensively adopted in the literature to address optimization problems in many different fields, such as industrial design, civil engineering, electrical and mechanical engineering problems. In order to ensure its search performance, HS requires extensive tuning of its four parameters control namely harmony memory size (HMS), harmony memory consideration rate (HMCR), pitch adjustment rate (PAR), and bandwidth (BW). However, tuning process is often cumbersome and is problem dependent. Furthermore, there is no one size fits all problems. Additionally, despite many useful works, HS and its variant still suffer from weak exploitation which can lead to poor convergence problem. Addressing these aforementioned issues, this thesis proposes to augment HS with adaptive tuning using Grey Wolf Optimizer (GWO). Meanwhile, to enhance its exploitation, this thesis also proposes to adopt a new variant of the opposition-based learning technique (OBL). Taken together, the proposed hybrid algorithm, called IHS-GWO, aims to address continuous optimization problems. The IHS-GWO is evaluated using two standard benchmarking sets and two real-world optimization problems. The first benchmarking set consists of 24 classical benchmark unimodal and multimodal functions whilst the second benchmark set contains 30 state-of-the-art benchmark functions from the Congress on Evolutionary Computation (CEC). The two real-world optimization problems involved the three-bar truss and spring design. Statistical analysis using Wilcoxon rank-sum and Friedman of IHS-GWO’s results with recent HS variants and other metaheuristic demonstrate superior performance. 2020-09 Thesis NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33729/1/Hybrid%20harmony%20search%20algorithm%20for%20continuous.pdf Ala’a Atallah, Hamad Alomoush (2020) Hybrid harmony search algorithm for continuous optimization problems. PhD thesis, Universiti Malaysia Pahang.
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Ala’a Atallah, Hamad Alomoush
Hybrid harmony search algorithm for continuous optimization problems
description Harmony Search (HS) algorithm has been extensively adopted in the literature to address optimization problems in many different fields, such as industrial design, civil engineering, electrical and mechanical engineering problems. In order to ensure its search performance, HS requires extensive tuning of its four parameters control namely harmony memory size (HMS), harmony memory consideration rate (HMCR), pitch adjustment rate (PAR), and bandwidth (BW). However, tuning process is often cumbersome and is problem dependent. Furthermore, there is no one size fits all problems. Additionally, despite many useful works, HS and its variant still suffer from weak exploitation which can lead to poor convergence problem. Addressing these aforementioned issues, this thesis proposes to augment HS with adaptive tuning using Grey Wolf Optimizer (GWO). Meanwhile, to enhance its exploitation, this thesis also proposes to adopt a new variant of the opposition-based learning technique (OBL). Taken together, the proposed hybrid algorithm, called IHS-GWO, aims to address continuous optimization problems. The IHS-GWO is evaluated using two standard benchmarking sets and two real-world optimization problems. The first benchmarking set consists of 24 classical benchmark unimodal and multimodal functions whilst the second benchmark set contains 30 state-of-the-art benchmark functions from the Congress on Evolutionary Computation (CEC). The two real-world optimization problems involved the three-bar truss and spring design. Statistical analysis using Wilcoxon rank-sum and Friedman of IHS-GWO’s results with recent HS variants and other metaheuristic demonstrate superior performance.
format Thesis
author Ala’a Atallah, Hamad Alomoush
author_facet Ala’a Atallah, Hamad Alomoush
author_sort Ala’a Atallah, Hamad Alomoush
title Hybrid harmony search algorithm for continuous optimization problems
title_short Hybrid harmony search algorithm for continuous optimization problems
title_full Hybrid harmony search algorithm for continuous optimization problems
title_fullStr Hybrid harmony search algorithm for continuous optimization problems
title_full_unstemmed Hybrid harmony search algorithm for continuous optimization problems
title_sort hybrid harmony search algorithm for continuous optimization problems
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/33729/1/Hybrid%20harmony%20search%20algorithm%20for%20continuous.pdf
http://umpir.ump.edu.my/id/eprint/33729/
_version_ 1731225794110291968
score 13.211869

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