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| 1. autor: | |
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| Format: | Recurso digital |
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| Wydane: |
Zenodo
2025
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| Dostęp online: | https://doi.org/10.5281/zenodo.15610091 |
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Spis treści:
- <p>Abstract <br>This paper presents a new sine-cosine <br>algorithm (SCA) that is improved by a <br>Differential Evolution (DE) hybridization <br>scheme to achieve maximum optimization <br>performance. The Hybridized SCA (H-SCA– <br>DE) algorithm enhances the convergence rate, <br>provides <br>an <br>efficient <br>balance between <br>exploration and exploitation, and improves the <br>overall precision. Its efficiency was confirmed <br>experimentally on twenty-three benchmark <br>functions, and its performance was compared <br>with that of the basic SCA and other <br>optimization methods. Experiments showed </p> <p>that H-SCA–DE provided better solution <br>quality in thirteen out of twenty-three test <br>cases, showing excellent performance in all but <br>one of the test conditions. The proposed <br>approach also exhibited higher stability and <br>robustness in various optimization scenarios. <br>The results imply that H-SCA–DE is a highly <br>efficient optimization algorithm with a quicker <br>and more consistent solution for complex real- <br>world problems. </p>