An enhanced escape algorithm with comprehensive learning and Cauchy–Gaussian mutation for reservoir optimization
Abstract Global optimization of complex, high-dimensional landscapes remains a fundamental challenge in scientific and engineering domains. To mitigate the inherent limitations of premature convergence and diversity loss, this paper proposes CLGMESC, an enhanced variant of the Escape Algorithm (ESC)...
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| Автори: | , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Nature Portfolio
2026-04-01
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| Серія: | Scientific Reports |
| Предмети: | |
| Онлайн доступ: | https://doi.org/10.1038/s41598-026-46087-8 |
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