MTV-SCA: multi-trial vector-based sine cosine algorithm

Nadimi-Shahraki, MH, Taghian, S, Javaheri, D, Sadiq, AS ORCID: https://orcid.org/0000-0002-5746-0257, Khodadadi, N and Mirjalili, S, 2024. MTV-SCA: multi-trial vector-based sine cosine algorithm. Cluster Computing. ISSN 1386-7857

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Abstract

The sine cosine algorithm (SCA) is a metaheuristic algorithm that employs the characteristics of sine and cosine trigonometric functions. SCA's deficiencies include a tendency to get trapped in local optima, exploration-exploitation imbalance, and poor accuracy, which limit its effectiveness in solving complex optimization problems. To address these limitations, a multi-trial vector-based sine cosine algorithm (MTV-SCA) is proposed in this study. In MTV-SCA, a sufficient number of search strategies incorporating three control parameters are adapted through a multi-trial vector (MTV) approach to achieve specific objectives during the search process. The major contribution of this study is employing four distinct search strategies, each adapted to preserve the equilibrium between exploration and exploitation and avoid premature convergence during optimization. The strategies utilize different sinusoidal and cosinusoidal parameters to improve the algorithm's performance. The effectiveness of MTV-SCA was evaluated using benchmark functions of CEC 2018 and compared to state-of-the-art, well-established, CEC 2017 winner algorithms and recent optimization algorithms. The results demonstrate that the MTV-SCA outperforms the traditional SCA and other optimization algorithms in terms of convergence speed, accuracy, and the capability to avoid premature convergence. Moreover, the Friedman and Wilcoxon signed-rank tests were employed to statistically analyze the experimental results, validating that the MTV-SCA significantly surpasses other comparative algorithms. The real-world applicability of this algorithm is also demonstrated by optimizing six non-convex constrained optimization problems in engineering design. The experimental results indicate that MTV-SCA can effectively handle complex optimization challenges.

Item Type:	Journal article
Publication Title:	Cluster Computing
Creators:	Nadimi-Shahraki, M.H., Taghian, S., Javaheri, D., Sadiq, A.S., Khodadadi, N. and Mirjalili, S.
Publisher:	Springer Nature
Date:	28 June 2024
ISSN:	1386-7857
Identifiers:	Number Type 10.1007/s10586-024-04602-4 DOI 1906869 Other
Rights:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10586-024-04602-4.
Divisions:	Schools > School of Science and Technology
Record created by:	Melissa Cornwell
Date Added:	01 Jul 2024 10:05
Last Modified:	01 Jul 2024 10:05
URI:	https://irep.ntu.ac.uk/id/eprint/51656

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