保存先:
| 第一著者: | |
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| フォーマット: | Recurso digital |
| 言語: | |
| 出版事項: |
Zenodo
2025
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| オンライン･アクセス: | https://doi.org/10.5281/zenodo.17852914 |
| タグ: |
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目次:
- <p><span>This paper proposes a novel optimization algorithm called Cryo-Entropy Gradient Dynamics (CEGD), which is inspired by three key phenomena in cryogenics and low-temperature physics: phase condensation, dilution flow, and quantum fluctuation and tunneling. The algorithm maps cryogenic physics phenomena to the optimization process, introducing three mechanisms: Dynamic Condensation Potential (DCP), Entropy-Guided Dilution Flow (EGF), and Coherent Quantum Tunneling (CQT), to achieve efficient search and local minima escape capabilities for non-convex optimization problems. The algorithm employs a dual-temperature mechanism: macroscopic temperature controls the global exploration and convergence rate, while microscopic temperature introduces periodic perturbations to simulate quantum fluctuations, thereby enhancing local exploration capabilities while maintaining convergence. This paper provides a detailed description of the algorithm's mathematical structure and presents complete iterative formulas and pseudocode. This method theoretically possesses excellent global search capabilities and adaptive characteristics, providing a new approach for the research of optimization algorithms inspired by low-temperature physics.</span></p>