Stellar oscillation optimizer: a nature-inspired metaheuristic optimization algorithm

• Published in: Cluster computing Vol. 28; no. 6; p. 362
• Main Authors: Rodan, Ali, Al-Tamimi, Abdel-Karimi, Al-Alnemer, Loai, Mirjalili, Seyedali
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2025; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Computer Communication Networks; Computer Science; Datasets; Design engineering; Design optimization; Foraging behavior; Heuristic methods; Image segmentation; Literature reviews; Operating Systems; Optimization; Optimization algorithms; Physical properties; Processor Architectures; Scheduling; Source code; Stellar oscillations; Stellar seismology; Traveling salesman problem; SOO; Metaheuristic; Global optimization; Swarm intelligence optimization; Physical optimizer; Grey wolf optimizer; Sine cosine optimization; Particle swarm optimization; Artificial intelligence; Stellar oscillation optimizer; Optimization; Heuristic
• ISSN: 1386-7857; 1573-7543; 1573-7543
• DOI: 10.1007/s10586-024-04976-5

This paper proposes a nature-inspired metaheuristic optimization algorithm called Stellar Oscillation Optimizer (SOO), SOO is inspired from the field of asteroseismology, which examines the oscillatory behavior of stars to understand their internal structures, physical properties, and evolutionary stages. SOO simulates the dynamic expansion and contraction phases observed in stellar pulsations for balancing exploration and exploitation. SOO was evaluated using the IEEE CEC2020 and CEC2022 benchmark datasets, which consist of 10 and 12 functions, respectively. In addition, SOO’s performance was tested on three real-world engineering design problems to further validate its effectiveness. SOO was benchmarked against 10 Physics and mathematics-inspired Optimizers, as well as 15 recent optimizers (2021-2024). SOO achieved first place in 6 out of 10 functions on the CEC2020 dataset when compared to 25 recent, physics and mathematics-inspired optimizers and ranked first in 9 out of 12 functions on the CEC2022 dataset when compared to the 15 recent optimizers. To assess the statistical significance of SOO’s performance, Wilcoxon’s signed-rank and Friedman’s tests were employed. The experimental results demonstrate that SOO consistently outperforms its competitors across a wide variety of optimization tasks. Moreover, SOO was successfully applied to multi-level image segmentation, highlighting its diversity in solving real-world practical applications. The source code of SOO is publicly available for both MATLAB at: ( https://www.mathworks.com/matlabcentral/fileexchange/161921-stellar-oscillation-optimizer-meta-heuristic-optimimization ) and PYTHON at: ( https://github.com/AliRodan/Stellar-Oscillation-Optimizer ) (For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Authors Accepted Manuscript version of this paper arising from this submission.).