Hybrid Generalized Normal Distribution Optimization with Sine Cosine Algorithm for Global Optimization
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Author(s)
Sadiq, Ali Safaa
Akbari, Hesam
Mong, Guo Ren
Mirjalili, Seyedali
Griffith University Author(s)
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Kim, JH
Deep, K
Geem, ZW
Sadollah, A
Yadav, A
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Seoul, South Korea; Online
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Abstract
This paper proposes two hybrid versions of the generalized normal distribution optimization (GNDO) and sine cosine algorithm (SCA) for global optimization. The proposed hybrid methods combine the excellent characteristics of the GNDO and SCA algorithms to enhance the exploration and exploitation behaviors. Moreover, an additional weight parameter is introduced to further improve the search ability of the hybrid methods. The proposed methods are tested with 23 mathematical optimization problems. Our results reveal that the proposed hybrid method was very competitive compared to the other metaheuristic algorithms.
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Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications
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140
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Subject
Data structures and algorithms
Distributed computing and systems software
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Engineering
Engineering, Multidisciplinary
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Citation
Too, J; Sadiq, AS; Akbari, H; Mong, GR; Mirjalili, S, Hybrid Generalized Normal Distribution Optimization with Sine Cosine Algorithm for Global Optimization, Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications, 2022, 140, pp. 35-42