Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization

An efficient optimization method is needed to address complicated problems and find optimal solutions. The gazelle optimization algorithm (GOA) is a global stochastic optimizer that is straightforward to comprehend and has powerful search capabilities. Nevertheless, the GOA is unsuitable for address...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Abu Zitar, Raed (author)
مؤلفون آخرون: Abualigah, Laith (author), Diabat, Ali (author)
منشور في: 2022
الموضوعات:
الوصول للمادة أونلاين:https://depot.sorbonne.ae/handle/20.500.12458/1335
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author Abu Zitar, Raed
author2 Abualigah, Laith
Diabat, Ali
author2_role author
author
author_facet Abu Zitar, Raed
Abualigah, Laith
Diabat, Ali
author_role author
dc.creator.none.fl_str_mv Abu Zitar, Raed
Abualigah, Laith
Diabat, Ali
dc.date.none.fl_str_mv 2022-12-05T05:02:34Z
2022-12-05T05:02:34Z
2022
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv 10.3390/math10234509
2227-7390
https://depot.sorbonne.ae/handle/20.500.12458/1335
10.3390/math10234509
dc.language.none.fl_str_mv en
dc.relation.none.fl_str_mv Mathematics
dc.subject.none.fl_str_mv orthogonal learning (OL)
Rosenbrock’s direct rotational (RDR)
gazelle optimization algorithm (GOA)
CEC2017
data clustering
optimization problems
dc.title.none.fl_str_mv Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization
dc.type.none.fl_str_mv Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article
description An efficient optimization method is needed to address complicated problems and find optimal solutions. The gazelle optimization algorithm (GOA) is a global stochastic optimizer that is straightforward to comprehend and has powerful search capabilities. Nevertheless, the GOA is unsuitable for addressing multimodal, hybrid functions, and data mining problems. Therefore, the current paper proposes the orthogonal learning (OL) method with Rosenbrock’s direct rotation strategy to improve the GOA and sustain the solution variety (IGOA). We performed comprehensive experiments based on various functions, including 23 classical and IEEE CEC2017 problems. Moreover, eight data clustering problems taken from the UCI repository were tested to verify the proposed method’s performance further. The IGOA was compared with several other proposed meta-heuristic algorithms. Moreover, the Wilcoxon signed-rank test further assessed the experimental results to conduct more systematic data analyses. The IGOA surpassed other comparative optimizers in terms of convergence speed and precision. The empirical results show that the proposed IGOA achieved better outcomes than the basic GOA and other state-of-the-art methods and performed better in terms of solution quality.
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identifier_str_mv 10.3390/math10234509
2227-7390
language_invalid_str_mv en
network_acronym_str sorbonner
network_name_str Sorbonne University Abu Dhabi repository
oai_identifier_str oai:depot.sorbonne.ae:20.500.12458/1335
publishDate 2022
repository.mail.fl_str_mv
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spelling Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global OptimizationAbu Zitar, RaedAbualigah, LaithDiabat, Aliorthogonal learning (OL)Rosenbrock’s direct rotational (RDR)gazelle optimization algorithm (GOA)CEC2017data clusteringoptimization problemsAn efficient optimization method is needed to address complicated problems and find optimal solutions. The gazelle optimization algorithm (GOA) is a global stochastic optimizer that is straightforward to comprehend and has powerful search capabilities. Nevertheless, the GOA is unsuitable for addressing multimodal, hybrid functions, and data mining problems. Therefore, the current paper proposes the orthogonal learning (OL) method with Rosenbrock’s direct rotation strategy to improve the GOA and sustain the solution variety (IGOA). We performed comprehensive experiments based on various functions, including 23 classical and IEEE CEC2017 problems. Moreover, eight data clustering problems taken from the UCI repository were tested to verify the proposed method’s performance further. The IGOA was compared with several other proposed meta-heuristic algorithms. Moreover, the Wilcoxon signed-rank test further assessed the experimental results to conduct more systematic data analyses. The IGOA surpassed other comparative optimizers in terms of convergence speed and precision. The empirical results show that the proposed IGOA achieved better outcomes than the basic GOA and other state-of-the-art methods and performed better in terms of solution quality.2022-12-05T05:02:34Z2022-12-05T05:02:34Z2022Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articleapplication/pdf10.3390/math102345092227-7390https://depot.sorbonne.ae/handle/20.500.12458/133510.3390/math10234509enMathematicsoai:depot.sorbonne.ae:20.500.12458/13352024-09-11T10:56:46Z
spellingShingle Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization
Abu Zitar, Raed
orthogonal learning (OL)
Rosenbrock’s direct rotational (RDR)
gazelle optimization algorithm (GOA)
CEC2017
data clustering
optimization problems
title Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization
title_full Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization
title_fullStr Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization
title_full_unstemmed Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization
title_short Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization
title_sort Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization
topic orthogonal learning (OL)
Rosenbrock’s direct rotational (RDR)
gazelle optimization algorithm (GOA)
CEC2017
data clustering
optimization problems
url https://depot.sorbonne.ae/handle/20.500.12458/1335