An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms

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dc.authorscopusidAmir Seyyedabbasi / 57202833910
dc.authorscopusidWadhah Zeyad Tareq Tareq / 56543609600
dc.authorwosidAmir Seyyedabbasi / HJH-7387-2023
dc.authorwosidWadhah Zeyad Tareq Tareq / GLS-2101-2022
dc.contributor.authorSeyyedabbasi, Amir
dc.contributor.authorTareq Tareq, Wadhah Zeyad
dc.contributor.authorBacanin, Nebojsa
dc.date.accessioned2025-04-18T07:39:34Z
dc.date.available2025-04-18T07:39:34Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractRecently, the Honey Badger Algorithm (HBA) was proposed as a metaheuristic algorithm. Honey badger hunting behaviour inspired the development of this algorithm. In the exploitation phase, HBA performs poorly and stagnates at the local best solution. On the other hand, the sand cat swarm optimization (SCSO) is a very competitive algorithm compared to other common metaheuristic algorithms since it has outstanding performance in the exploitation phase. Hence, the purpose of this paper is to hybridize HBA with SCSO so that the SCSO can overcome deficiencies of the HBA to improve the quality of the solution. The SCSO can effectively exploit optimal solutions. For the research conducted in this paper, a hybrid metaheuristic algorithm called HBASCSO was developed. The proposed approach was evaluated against challenging CEC benchmark instances taken from CEC2015, CEC2017, and CEC2019 benchmark suites The HBASCSO is also evaluated concerning the original HBA, SCSO, as well as several other recently proposed algorithms. To demonstrate that the proposed method performs significantly better than other competitive algorithms, 30 independent runs of each algorithm were evaluated to determine the best, worst, mean, and standard deviation of fitness functions. In addition, the Wilcoxon rank-sum test is used as a non-parametric comparison, and it has been found that the proposed algorithm outperforms other algorithms. Hence, the HBASCSO achieves an optimum solution that is better than the original algorithms.
dc.identifier.citationSeyyedabbasi, A., Tareq Tareq, W. Z., & Bacanin, N. (2024). An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms. Multimedia Tools and Applications, 1-36.
dc.identifier.doi10.1007/s11042-024-19437-9
dc.identifier.endpage85138
dc.identifier.issn13807501
dc.identifier.issue37
dc.identifier.scopus2-s2.0-85194723849
dc.identifier.scopusqualityQ1
dc.identifier.startpage85103
dc.identifier.urihttp://dx.doi.org/10.1007/s11042-024-19437-9
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6472
dc.identifier.volume83
dc.indekslendigikaynakScopus
dc.institutionauthorSeyyedabbasi, Amir
dc.institutionauthorTareq Tareq, Wadhah Zeyad
dc.institutionauthoridAmir Seyyedabbasi / 0000-0001-5186-4499
dc.institutionauthoridWadhah Zeyad Tareq Tareq / 0000-0003-4571-0295
dc.language.isoen
dc.publisherSpringerLink
dc.relation.ispartofMultimedia Tools and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBenchmark Functions
dc.subjectHoney Badger Algorithm
dc.subjectHybrid Metaheuristic
dc.subjectMetaheuristic Algorithm
dc.subjectSand Cat Swarm Optimization
dc.titleAn Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms
dc.typeArticle

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