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沙普利指导的全球优化算法与集成光学反向设计中的应用

Mohamed Sadek Radwan, Sean Hooten, Thomas Van Vaerenbergh

|August 13, 2025

概括

一个新的Shapley引导的随机优化 (SGSO) 算法使用Shapley值来实现高效的全球优化. 它成功设计了光子结构,超越了现有的方法,并提供了更便宜的计算替代方案.

科学领域:

计算物理学的计算物理.
材料科学是一种材料科学.
优化算法的优化算法

背景情况:

全球优化对于复杂的设计问题至关重要.
现有的元启发式技术可能是计算密集的.
反向设计需要有效的搜索策略.

研究的目的:

介绍沙普利指导的随机优化 (SGSO) 算法.
验证SGSO在基准函数和现实世界的光子结构设计上的效率.
建议简化沙普利值计算以降低成本.

主要方法:

开发了沙普利指导的随机优化 (SGSO) 算法.
在Easom和Ackley基准函数上测试了SGSO.
将SGSO应用于3dB分离器,格子合器和多层宽带镜的反向设计.
将SGSO与盆地跳跃进行基准测试,并分析了与遗传算法的连接.

主要成果:

SGSO有效地引导了对光子结构的最佳解决方案的搜索.
该算法展示了高性能和计算效率.
一种简化的沙普利值计算方法实现了令人满意的收,成本更低.
SGSO显示出在复杂的优化场景中导航的潜力,与既有方法相比.

结论:

SGSO是一种能够解决复杂的逆向设计问题的优化算法.
简化的Shapley值计算增强了SGSO的实际应用性.
SGSO为光子设备设计提供了对现有的元启发式优化技术的有希望的替代方案.

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