Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems

Multi-objective swarm intelligence-based (MOSI-based) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) with conflicting objectives. Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point appr...

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Main Author:	Yasear, Shaymah Akram
Format:	Thesis
Language:	eng eng eng
Published:	2020
Subjects:	QA Mathematics
Online Access:	https://etd.uum.edu.my/8673/1/Deposit%20Permission_s901761.pdf https://etd.uum.edu.my/8673/2/s901761_01.pdf https://etd.uum.edu.my/8673/3/s901761_references.docx
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spelling	my-uum-etd.86732021-09-27T06:50:45Z Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems 2020 Yasear, Shaymah Akram Ku Mahamud, Ku Ruhana Alobaedy, Mustafa Muwafak Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences QA Mathematics Multi-objective swarm intelligence-based (MOSI-based) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) with conflicting objectives. Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. The reference point is determined by the decision maker to guide the search process to a particular region in the true Pareto front. However, HHMO algorithm produces a poor approximation to the Pareto front because lack of information sharing in its population update strategy, equal division of convergence parameter and randomly generated initial population. A two-step enhanced non-dominated sorting HHMO (2SENDSHHMO) algorithm has been proposed to solve this problem. The algorithm includes (i) a population update strategy which improves the movement of hawks in the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. The population update strategy calculates a new position of hawks based on the flush-and-ambush technique of Harris’s hawks, and selects the best hawks based on the non-dominated sorting approach. The adjustment strategy enables the parameter to adaptively changed based on the state of the search space. The initial population is produced by generating quasi-random numbers using Rsequence followed by adapting the partial opposition-based learning concept to improve the diversity of the worst half in the population of hawks. The performance of the 2S-ENDSHHMO has been evaluated using 12 MOPs and three engineering MOPs. The obtained results were compared with the results of eight state-of-the-art multi-objective optimization algorithms. The 2S-ENDSHHMO algorithm was able to generate non-dominated solutions with greater convergence and diversity in solving most MOPs and showed a great ability in jumping out of local optima. This indicates the capability of the algorithm in exploring the search space. The 2S-ENDSHHMO algorithm can be used to improve the search process of other MOSI-based algorithms and can be applied to solve MOPs in applications such as structural design and signal processing. 2020 Thesis https://etd.uum.edu.my/8673/ https://etd.uum.edu.my/8673/1/Deposit%20Permission_s901761.pdf text eng staffonly https://etd.uum.edu.my/8673/2/s901761_01.pdf text eng public https://etd.uum.edu.my/8673/3/s901761_references.docx text eng public other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng
advisor	Ku Mahamud, Ku Ruhana Alobaedy, Mustafa Muwafak
topic	QA Mathematics
spellingShingle	QA Mathematics Yasear, Shaymah Akram Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
description	Multi-objective swarm intelligence-based (MOSI-based) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) with conflicting objectives. Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. The reference point is determined by the decision maker to guide the search process to a particular region in the true Pareto front. However, HHMO algorithm produces a poor approximation to the Pareto front because lack of information sharing in its population update strategy, equal division of convergence parameter and randomly generated initial population. A two-step enhanced non-dominated sorting HHMO (2SENDSHHMO) algorithm has been proposed to solve this problem. The algorithm includes (i) a population update strategy which improves the movement of hawks in the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. The population update strategy calculates a new position of hawks based on the flush-and-ambush technique of Harris’s hawks, and selects the best hawks based on the non-dominated sorting approach. The adjustment strategy enables the parameter to adaptively changed based on the state of the search space. The initial population is produced by generating quasi-random numbers using Rsequence followed by adapting the partial opposition-based learning concept to improve the diversity of the worst half in the population of hawks. The performance of the 2S-ENDSHHMO has been evaluated using 12 MOPs and three engineering MOPs. The obtained results were compared with the results of eight state-of-the-art multi-objective optimization algorithms. The 2S-ENDSHHMO algorithm was able to generate non-dominated solutions with greater convergence and diversity in solving most MOPs and showed a great ability in jumping out of local optima. This indicates the capability of the algorithm in exploring the search space. The 2S-ENDSHHMO algorithm can be used to improve the search process of other MOSI-based algorithms and can be applied to solve MOPs in applications such as structural design and signal processing.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Yasear, Shaymah Akram
author_facet	Yasear, Shaymah Akram
author_sort	Yasear, Shaymah Akram
title	Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
title_short	Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
title_full	Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
title_fullStr	Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
title_full_unstemmed	Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
title_sort	enhanced harris's hawk algorithm for continuous multi-objective optimization problems
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2020
url	https://etd.uum.edu.my/8673/1/Deposit%20Permission_s901761.pdf https://etd.uum.edu.my/8673/2/s901761_01.pdf https://etd.uum.edu.my/8673/3/s901761_references.docx
_version_	1747828436629454848

Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems

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