Coevolutionary Emergent Systems Optimization with Applications to Ultra-High-Dimensional Metasurface Design : OAM Wave Manipulation

Zhengxuan Jiang, Guowen Ding, Wen Jiang
Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, PMLR 286:1876-1894, 2025.

Abstract

Optimization problems in electromagnetic wave manipulation and metasurface design are becoming increasingly high-dimensional, often involving thousands of variables that need precise control. Traditional optimization algorithms face significant challenges in maintaining both accuracy and computational efficiency when dealing with such ultra-high-dimensional problems. This paper presents a novel Coevolutionary Emergent Systems Optimization (CESO) algorithm that integrates coevolutionary dynamics, emergent behavior, and adaptive mechanisms to address these challenges. CESO features a unique multi-subsystem architecture that enables parallel exploration of solution spaces while maintaining interactive influences between subsystems. The algorithm incorporates an efficient adaptive mechanism for parameter adjustment and a distinctive emergent behavior simulation mechanism that prevents local optima traps through periodic subsystem reorganization. Experimental results on the CEC2017 benchmark suite demonstrate CESO’s superior performance. The algorithm’s practical effectiveness is validated through a challenging application in electromagnetic wave manipulation - OAM wave demultiplexing system (10,000 dimensions). In this application, CESO achieves superior mode separation for OAM wave demultiplexing compared to traditional algorithms. These results demonstrate CESO’s significant advantages in solving practical high-dimensional optimization problems.

Cite this Paper

BibTeX
@InProceedings{pmlr-v286-jiang25b, title = {Coevolutionary Emergent Systems Optimization with Applications to Ultra-High-Dimensional Metasurface Design : OAM Wave Manipulation}, author = {Jiang, Zhengxuan and Ding, Guowen and Jiang, Wen}, booktitle = {Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence}, pages = {1876--1894}, year = {2025}, editor = {Chiappa, Silvia and Magliacane, Sara}, volume = {286}, series = {Proceedings of Machine Learning Research}, month = {21--25 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v286/main/assets/jiang25b/jiang25b.pdf}, url = {https://proceedings.mlr.press/v286/jiang25b.html}, abstract = {Optimization problems in electromagnetic wave manipulation and metasurface design are becoming increasingly high-dimensional, often involving thousands of variables that need precise control. Traditional optimization algorithms face significant challenges in maintaining both accuracy and computational efficiency when dealing with such ultra-high-dimensional problems. This paper presents a novel Coevolutionary Emergent Systems Optimization (CESO) algorithm that integrates coevolutionary dynamics, emergent behavior, and adaptive mechanisms to address these challenges. CESO features a unique multi-subsystem architecture that enables parallel exploration of solution spaces while maintaining interactive influences between subsystems. The algorithm incorporates an efficient adaptive mechanism for parameter adjustment and a distinctive emergent behavior simulation mechanism that prevents local optima traps through periodic subsystem reorganization. Experimental results on the CEC2017 benchmark suite demonstrate CESO’s superior performance. The algorithm’s practical effectiveness is validated through a challenging application in electromagnetic wave manipulation - OAM wave demultiplexing system (10,000 dimensions). In this application, CESO achieves superior mode separation for OAM wave demultiplexing compared to traditional algorithms. These results demonstrate CESO’s significant advantages in solving practical high-dimensional optimization problems.} }
Endnote
%0 Conference Paper %T Coevolutionary Emergent Systems Optimization with Applications to Ultra-High-Dimensional Metasurface Design : OAM Wave Manipulation %A Zhengxuan Jiang %A Guowen Ding %A Wen Jiang %B Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2025 %E Silvia Chiappa %E Sara Magliacane %F pmlr-v286-jiang25b %I PMLR %P 1876--1894 %U https://proceedings.mlr.press/v286/jiang25b.html %V 286 %X Optimization problems in electromagnetic wave manipulation and metasurface design are becoming increasingly high-dimensional, often involving thousands of variables that need precise control. Traditional optimization algorithms face significant challenges in maintaining both accuracy and computational efficiency when dealing with such ultra-high-dimensional problems. This paper presents a novel Coevolutionary Emergent Systems Optimization (CESO) algorithm that integrates coevolutionary dynamics, emergent behavior, and adaptive mechanisms to address these challenges. CESO features a unique multi-subsystem architecture that enables parallel exploration of solution spaces while maintaining interactive influences between subsystems. The algorithm incorporates an efficient adaptive mechanism for parameter adjustment and a distinctive emergent behavior simulation mechanism that prevents local optima traps through periodic subsystem reorganization. Experimental results on the CEC2017 benchmark suite demonstrate CESO’s superior performance. The algorithm’s practical effectiveness is validated through a challenging application in electromagnetic wave manipulation - OAM wave demultiplexing system (10,000 dimensions). In this application, CESO achieves superior mode separation for OAM wave demultiplexing compared to traditional algorithms. These results demonstrate CESO’s significant advantages in solving practical high-dimensional optimization problems.
APA
Jiang, Z., Ding, G. & Jiang, W.. (2025). Coevolutionary Emergent Systems Optimization with Applications to Ultra-High-Dimensional Metasurface Design : OAM Wave Manipulation. Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 286:1876-1894 Available from https://proceedings.mlr.press/v286/jiang25b.html.

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