Fuzzy adaptive emperor penguin optimizer for global optimization problems

The Emperor Penguin Optimizer (EPO) is a recently developed population-based metaheuristic algorithm that simulates the huddling behaviour of emperor penguins. Mixed results have been observed in the performance of EPO in solving general optimization problems. Within the EPO, two parameters need to...

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Main Author:	Md Abdul, Kader
Format:	Thesis
Language:	English
Published:	2023
Subjects:	QA75 Electronic computers Computer science QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/39231/1/ir.Fuzzy%20adaptive%20emperor%20penguin%20optimizer%20for%20global%20optimization%20problems.pdf
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spelling	my-ump-ir.392312023-11-08T07:44:36Z Fuzzy adaptive emperor penguin optimizer for global optimization problems 2023-04 Md Abdul, Kader QA75 Electronic computers. Computer science QA76 Computer software The Emperor Penguin Optimizer (EPO) is a recently developed population-based metaheuristic algorithm that simulates the huddling behaviour of emperor penguins. Mixed results have been observed in the performance of EPO in solving general optimization problems. Within the EPO, two parameters need to be tuned (namely f and l) to ensure a good balance between exploration (i.e., roaming unknown locations) and exploitation (i.e., manipulating the current known best). Since the search contour varies depending on the optimization problem, the tuning of parameters f and l is problem-dependent, and there is no one-size-fits-all approach. To alleviate this parameter tuning problem, an adaptive mechanism can be introduced in EPO. This research work proposes a fuzzy adaptive variant of EPO, namely FAEPO, to solve this problem. As the name suggests, FAEPO can adaptively tune the parameters f and l throughout the search based on three measures (i.e., quality, success rate, and diversity of the current search) via fuzzy decisions. A test suite of twelve benchmark test functions and three global optimization problems: Team Formation Optimization (TFO), Low Autocorrelation Binary Sequence (LABS), and Modified Condition/ Decision coverage (MC/DC) test case generation problem were solved using the proposed algorithm. The respective solution results of the competing metaheuristic algorithms were compared. The experimental results demonstrate that FAEPO significantly improved the performance especially of its predecessor (EPO), an improved variant of EPO (i.e., IEPO), and a fuzzy-based variant of ChOA (i.e., FChOA) and gives superior performance against the competing metaheuristic algorithms. Moreover, the proposed FAEPO requires 50% less fitness function evaluation in each iteration than the ancestor EPO and exhibits competitive performance in terms of convergence and computational time against its predecessor (EPO) and other competing meta-heuristic algorithms with a 90% confidence level. 2023-04 Thesis http://umpir.ump.edu.my/id/eprint/39231/ http://umpir.ump.edu.my/id/eprint/39231/1/ir.Fuzzy%20adaptive%20emperor%20penguin%20optimizer%20for%20global%20optimization%20problems.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Computing Kamal Zuhairi, Zamli
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Kamal Zuhairi, Zamli
topic	QA75 Electronic computers Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers Computer science QA76 Computer software Md Abdul, Kader Fuzzy adaptive emperor penguin optimizer for global optimization problems
description	The Emperor Penguin Optimizer (EPO) is a recently developed population-based metaheuristic algorithm that simulates the huddling behaviour of emperor penguins. Mixed results have been observed in the performance of EPO in solving general optimization problems. Within the EPO, two parameters need to be tuned (namely f and l) to ensure a good balance between exploration (i.e., roaming unknown locations) and exploitation (i.e., manipulating the current known best). Since the search contour varies depending on the optimization problem, the tuning of parameters f and l is problem-dependent, and there is no one-size-fits-all approach. To alleviate this parameter tuning problem, an adaptive mechanism can be introduced in EPO. This research work proposes a fuzzy adaptive variant of EPO, namely FAEPO, to solve this problem. As the name suggests, FAEPO can adaptively tune the parameters f and l throughout the search based on three measures (i.e., quality, success rate, and diversity of the current search) via fuzzy decisions. A test suite of twelve benchmark test functions and three global optimization problems: Team Formation Optimization (TFO), Low Autocorrelation Binary Sequence (LABS), and Modified Condition/ Decision coverage (MC/DC) test case generation problem were solved using the proposed algorithm. The respective solution results of the competing metaheuristic algorithms were compared. The experimental results demonstrate that FAEPO significantly improved the performance especially of its predecessor (EPO), an improved variant of EPO (i.e., IEPO), and a fuzzy-based variant of ChOA (i.e., FChOA) and gives superior performance against the competing metaheuristic algorithms. Moreover, the proposed FAEPO requires 50% less fitness function evaluation in each iteration than the ancestor EPO and exhibits competitive performance in terms of convergence and computational time against its predecessor (EPO) and other competing meta-heuristic algorithms with a 90% confidence level.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Md Abdul, Kader
author_facet	Md Abdul, Kader
author_sort	Md Abdul, Kader
title	Fuzzy adaptive emperor penguin optimizer for global optimization problems
title_short	Fuzzy adaptive emperor penguin optimizer for global optimization problems
title_full	Fuzzy adaptive emperor penguin optimizer for global optimization problems
title_fullStr	Fuzzy adaptive emperor penguin optimizer for global optimization problems
title_full_unstemmed	Fuzzy adaptive emperor penguin optimizer for global optimization problems
title_sort	fuzzy adaptive emperor penguin optimizer for global optimization problems
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Computing
publishDate	2023
url	http://umpir.ump.edu.my/id/eprint/39231/1/ir.Fuzzy%20adaptive%20emperor%20penguin%20optimizer%20for%20global%20optimization%20problems.pdf
_version_	1783732302339112960

Fuzzy adaptive emperor penguin optimizer for global optimization problems

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