Abstract
This study introduces an innovative optimization technique rooted in hybridizing the Golden Sine Algorithm II and the Non - Monopolized Search algorithm tailored to address unconstrained problems. The core concept underlying Golden Sine Algorithm II hinges on leveraging the diminishing pattern of the sine function and the golden ratio to navigate the solution landscape effectively; meanwhile, the Non-Monopolized Search is employed to improve the exploitation as a local search mechanism. Our proposal is called improved Golden Sine Algorithm II with Non-Monopolized Local Search (GSII-LS). Notably, GSII-LS is designed to complement and enhance existing optimization methodologies, working in synergy with non-monopolizing search strategies. To assess its efficacy, GSII-LS is subjected to rigorous testing across 34 benchmark functions for unconstrained optimization. Comparative analysis against optimization algorithms is conducted using established evaluation criteria. Results demonstrate that GSII-LS consistently achieves superior convergence towards global optima across numerous benchmark functions.
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Valdivia, A., Aranguren, I., Ramos-Frutos, J., Casas-Ordaz, A., Oliva, D., Zapotecas-Martínez, S. (2024). Improved Golden Sine II in Synergy with Non-monopolized Local Search Strategy. In: Sevaux, M., Olteanu, AL., Pardo, E.G., Sifaleras, A., Makboul, S. (eds) Metaheuristics. MIC 2024. Lecture Notes in Computer Science, vol 14754. Springer, Cham. https://doi.org/10.1007/978-3-031-62922-8_19
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