Local search enhanced Aquila optimization algorithm ameliorated with an ensemble of Wavelet mutation strategies for complex optimization problems

Basit Öğe Kaydı
dc.authoridTurgut, Mert Sinan/0000-0002-5739-2119
dc.contributor.authorTurgut, Oğuz Emrah
dc.contributor.authorTurgut, Mert Sinan
dc.date.accessioned2023-03-22T19:47:31Z
dc.date.available2023-03-22T19:47:31Z
dc.date.issued2023
dc.departmentBelirleneceken_US
dc.description.abstractAquila Optimization Algorithm (AQUILA) is a newly emerged metaheuristic optimizer for solving global optimization problems, which is based on intrinsic hunting behaviors of the foraging aquila individuals. However, this stochastic optimization method suffers from some algorithm-specific drawbacks, such as premature convergence to the local optimum points over the search hyperspace due to the lack of solution diversity in the population. To conquer this algorithmic deficiency, an ensemble of Wavelet mutation operators has been implemented into the standard AQUILA to enhance the explorative capabilities of the algorithm by diversifying the search domain as much as possible. Furthermore, a brand-new local search scheme empowered by the synergetic interactions of elite opposition-based learning and a simple-yet-effective exploitative manipulation equation is introduced into the base AQUILA to intensify on the previously visited promising regions. The proposed learning schemes are stochastically applied to the obtained solutions from the base Aquila algorithm to refine the overall solution quality and amend the premature convergence problem. It is also aimed to investigate whether the collective application of Wavelet mutation operators with different types entails a significant improvement in the general search effectivity of the algorithm rather than their individual efforts. Numerical experiments made on a suite of unconstrained unimodal and multimodal benchmark functions reveal that this hybridization with AQUILA has improved the general solution accuracy and stability to very high standards, outperforming its contemporary counterparts in the comparative statistical analysis. Furthermore, an exhaustive benchmark analysis has been performed on fourteen constrained real-world complex engineering problems.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.matcom.2022.11.020
dc.identifier.endpage374en_US
dc.identifier.issn0378-4754
dc.identifier.issn1872-7166
dc.identifier.scopus2-s2.0-85143664100en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage302en_US
dc.identifier.urihttps://doi.org/10.1016/j.matcom.2022.11.020
dc.identifier.urihttps://hdl.handle.net/20.500.14034/749
dc.identifier.volume206en_US
dc.identifier.wosWOS:000895766200001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.journalMathematics And Computers In Simulationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAquila optimization algorithmen_US
dc.subjectConstrained optimizationen_US
dc.subjectLocal searchen_US
dc.subjectMexican-hat waveleten_US
dc.subjectWavelet mutation operatoren_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectDifferential Evolutionen_US
dc.subjectGlobal Optimizationen_US
dc.subjectDesign Optimizationen_US
dc.subjectPerformanceen_US
dc.subjectMechanismen_US
dc.titleLocal search enhanced Aquila optimization algorithm ameliorated with an ensemble of Wavelet mutation strategies for complex optimization problemsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Elektrik-Elektronik Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu