An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SpringerLink
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Recently, 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.
Açıklama
Anahtar Kelimeler
Benchmark Functions, Honey Badger Algorithm, Hybrid Metaheuristic, Metaheuristic Algorithm, Sand Cat Swarm Optimization
Kaynak
Multimedia Tools and Applications
WoS Q Değeri
Scopus Q Değeri
Q1
Cilt
83
Sayı
37
Künye
Seyyedabbasi, A., Tareq Tareq, W. Z., & Bacanin, N. (2024). An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms. Multimedia Tools and Applications, 1-36.
