Improved Bat Algorithm for faster convergence in solving optimisation problem

Optimisation is concerned with finding solutions to problems under certain constraints. One of the optimisation approaches is metaheuristic. Metaheuristic algorithms are inspired by nature and utilise intelligent mechanisms. In this study, one of the metaheuristic algorithms known as the Bat Algorit...

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Main Author:	Ramli, Mohamad Raziff
Format:	Thesis
Language:	English English
Published:	2021
Subjects:	Q Science (General) QA Mathematics
Online Access:	http://eprints.utem.edu.my/id/eprint/26076/1/Improved%20Bat%20Algorithm%20for%20faster%20convergence%20in%20solving%20optimisation%20problem.pdf http://eprints.utem.edu.my/id/eprint/26076/2/Improved%20Bat%20Algorithm%20for%20faster%20convergence%20in%20solving%20optimisation%20problem.pdf
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spelling	my-utem-ep.260762023-01-13T16:05:31Z Improved Bat Algorithm for faster convergence in solving optimisation problem 2021 Ramli, Mohamad Raziff Q Science (General) QA Mathematics Optimisation is concerned with finding solutions to problems under certain constraints. One of the optimisation approaches is metaheuristic. Metaheuristic algorithms are inspired by nature and utilise intelligent mechanisms. In this study, one of the metaheuristic algorithms known as the Bat Algorithm (BA) has been discussed. Previous research has shown that BA is able to provide a good exploration and exploitation in finding solutions. However, this standard BA has the tendency to be trapped in a local minimum when applied to high dimensional search spaces besides experiencing slow convergence rate and low accuracy. The standard BA may be improved by integrating it with additional techniques which can increase its robustness through faster convergence and eventually producing more accurate results. Thus, this study proposed an Improved Bat Algorithm (IBA) by introducing some modifications to the standard BA. The additional techniques included are inertia weight factor, modified new bat position and adaptive boundary size. The IBA is evaluated and tested through a sequence of experiments conducted with ten benchmark functions. For comparison, three established algorithms namely Harmony Search (HS), Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) are analysed through the same set of experiments and compared with the IBA. The results show that the IBA performs better than Harmony Search (HS), Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). Despite the high dimensionality of the boundary size, the IBA is still able to produce significant results with the small number of iterations and fast convergence compared to other algorithms. Besides that, IBA was found comparable with existing variants of BA such as the IBA developed from the previous researcher in the year 2013 and the Hybrid Self-Adaptive Bat Algorithm (HSABA) developed in the year 2014. Finally, the developed IBA is found consistent with the exact method which is the simplex method when tested through fairness nurse scheduling problem. Therefore, this confirms the validity of the IBA as an alternative algorithm for solving optimisation problems. 2021 Thesis http://eprints.utem.edu.my/id/eprint/26076/ http://eprints.utem.edu.my/id/eprint/26076/1/Improved%20Bat%20Algorithm%20for%20faster%20convergence%20in%20solving%20optimisation%20problem.pdf text en public http://eprints.utem.edu.my/id/eprint/26076/2/Improved%20Bat%20Algorithm%20for%20faster%20convergence%20in%20solving%20optimisation%20problem.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=121246 phd doctoral Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology Abal Abas, Zuraida
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Abal Abas, Zuraida
topic	Q Science (General) QA Mathematics
spellingShingle	Q Science (General) QA Mathematics Ramli, Mohamad Raziff Improved Bat Algorithm for faster convergence in solving optimisation problem
description	Optimisation is concerned with finding solutions to problems under certain constraints. One of the optimisation approaches is metaheuristic. Metaheuristic algorithms are inspired by nature and utilise intelligent mechanisms. In this study, one of the metaheuristic algorithms known as the Bat Algorithm (BA) has been discussed. Previous research has shown that BA is able to provide a good exploration and exploitation in finding solutions. However, this standard BA has the tendency to be trapped in a local minimum when applied to high dimensional search spaces besides experiencing slow convergence rate and low accuracy. The standard BA may be improved by integrating it with additional techniques which can increase its robustness through faster convergence and eventually producing more accurate results. Thus, this study proposed an Improved Bat Algorithm (IBA) by introducing some modifications to the standard BA. The additional techniques included are inertia weight factor, modified new bat position and adaptive boundary size. The IBA is evaluated and tested through a sequence of experiments conducted with ten benchmark functions. For comparison, three established algorithms namely Harmony Search (HS), Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) are analysed through the same set of experiments and compared with the IBA. The results show that the IBA performs better than Harmony Search (HS), Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). Despite the high dimensionality of the boundary size, the IBA is still able to produce significant results with the small number of iterations and fast convergence compared to other algorithms. Besides that, IBA was found comparable with existing variants of BA such as the IBA developed from the previous researcher in the year 2013 and the Hybrid Self-Adaptive Bat Algorithm (HSABA) developed in the year 2014. Finally, the developed IBA is found consistent with the exact method which is the simplex method when tested through fairness nurse scheduling problem. Therefore, this confirms the validity of the IBA as an alternative algorithm for solving optimisation problems.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ramli, Mohamad Raziff
author_facet	Ramli, Mohamad Raziff
author_sort	Ramli, Mohamad Raziff
title	Improved Bat Algorithm for faster convergence in solving optimisation problem
title_short	Improved Bat Algorithm for faster convergence in solving optimisation problem
title_full	Improved Bat Algorithm for faster convergence in solving optimisation problem
title_fullStr	Improved Bat Algorithm for faster convergence in solving optimisation problem
title_full_unstemmed	Improved Bat Algorithm for faster convergence in solving optimisation problem
title_sort	improved bat algorithm for faster convergence in solving optimisation problem
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Information and Communication Technology
publishDate	2021
url	http://eprints.utem.edu.my/id/eprint/26076/1/Improved%20Bat%20Algorithm%20for%20faster%20convergence%20in%20solving%20optimisation%20problem.pdf http://eprints.utem.edu.my/id/eprint/26076/2/Improved%20Bat%20Algorithm%20for%20faster%20convergence%20in%20solving%20optimisation%20problem.pdf
_version_	1776103132954099712

Improved Bat Algorithm for faster convergence in solving optimisation problem

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