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An experimental study of a fuzzy adaptive emperor penguin optimizer for global optimization problem

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

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Main Authors: Kader, Md. Abdul, Zamli, Kamal Z., Alkazemi, Basem Yousef
Format: Article
Language:English
Published: IEEE 2022
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/35764/1/An%20experimental%20study%20of%20a%20fuzzy%20adaptive.pdf
http://umpir.ump.edu.my/id/eprint/35764/
https://doi.org/10.1109/ACCESS.2022.3213805
https://doi.org/10.1109/ACCESS.2022.3213805
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spelling my.ump.umpir.357642022-11-25T01:34:39Z http://umpir.ump.edu.my/id/eprint/35764/ An experimental study of a fuzzy adaptive emperor penguin optimizer for global optimization problem Kader, Md. Abdul Zamli, Kamal Z. Alkazemi, Basem Yousef QA76 Computer software Emperor Penguin Optimizer (EPO) is a recently developed population-based meta-heuristic algorithm that simulates the huddling behavior of emperor penguins. Mixed results have been observed on 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 f and l is problem-dependent, and there is no one-size-fits-all approach. To alleviate these problems, an adaptive mechanism can be introduced in EPO. This paper proposes a fuzzy adaptive variant of EPO, namely Fuzzy Adaptive Emperor Penguin Optimizer (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 optimization 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) were solved using the proposed algorithm. The respective solution results of the benchmark meta-heuristic algorithms were compared. The experimental results demonstrate that FAEPO significantly improved the performance of its predecessor (EPO) and gives superior performance against the competing meta-heuristic algorithms, including an improved variant of EPO (IEPO). IEEE 2022 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/35764/1/An%20experimental%20study%20of%20a%20fuzzy%20adaptive.pdf Kader, Md. Abdul and Zamli, Kamal Z. and Alkazemi, Basem Yousef (2022) An experimental study of a fuzzy adaptive emperor penguin optimizer for global optimization problem. IEEE Access, 10. 116344 -116374. ISSN 2169-3536 (In Press) https://doi.org/10.1109/ACCESS.2022.3213805 https://doi.org/10.1109/ACCESS.2022.3213805
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
Kader, Md. Abdul
Zamli, Kamal Z.
Alkazemi, Basem Yousef
An experimental study of a fuzzy adaptive emperor penguin optimizer for global optimization problem
description Emperor Penguin Optimizer (EPO) is a recently developed population-based meta-heuristic algorithm that simulates the huddling behavior of emperor penguins. Mixed results have been observed on 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 f and l is problem-dependent, and there is no one-size-fits-all approach. To alleviate these problems, an adaptive mechanism can be introduced in EPO. This paper proposes a fuzzy adaptive variant of EPO, namely Fuzzy Adaptive Emperor Penguin Optimizer (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 optimization 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) were solved using the proposed algorithm. The respective solution results of the benchmark meta-heuristic algorithms were compared. The experimental results demonstrate that FAEPO significantly improved the performance of its predecessor (EPO) and gives superior performance against the competing meta-heuristic algorithms, including an improved variant of EPO (IEPO).
format Article
author Kader, Md. Abdul
Zamli, Kamal Z.
Alkazemi, Basem Yousef
author_facet Kader, Md. Abdul
Zamli, Kamal Z.
Alkazemi, Basem Yousef
author_sort Kader, Md. Abdul
title An experimental study of a fuzzy adaptive emperor penguin optimizer for global optimization problem
title_short An experimental study of a fuzzy adaptive emperor penguin optimizer for global optimization problem
title_full An experimental study of a fuzzy adaptive emperor penguin optimizer for global optimization problem
title_fullStr An experimental study of a fuzzy adaptive emperor penguin optimizer for global optimization problem
title_full_unstemmed An experimental study of a fuzzy adaptive emperor penguin optimizer for global optimization problem
title_sort experimental study of a fuzzy adaptive emperor penguin optimizer for global optimization problem
publisher IEEE
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/35764/1/An%20experimental%20study%20of%20a%20fuzzy%20adaptive.pdf
http://umpir.ump.edu.my/id/eprint/35764/
https://doi.org/10.1109/ACCESS.2022.3213805
https://doi.org/10.1109/ACCESS.2022.3213805
_version_ 1751536400164978688
score 13.211869

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