arXiv AI
Sep 23

Artificial Neural Networks as Surrogate Models in Black Box Optimization

arXiv:2609.22329v1 Announce Type: cross Abstract: Black-Box Optimization (BBO) is often applied in several engineering fields and can utilize an advancement of numerical measure- ments and simulation...

By Md Khadimul Islam Zim (Czech Academy of Sciences, Institute of Computer Science, Prague, Czech Republic), Martin Hole\v{n}a (Czech Academy of Sciences, Institute of Computer Science, Prague, Czech Republic)
arXiv AI
Oct 9

Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating

The paper introduces DRL-DCO, a Deep Reinforcement Learning‑governed dynamic orchestration of Differential Evolution and CMA‑ES for high‑dimensional structural model updating. A DDPG actor‑critic agent uses a progression‑aware state and diversity‑informed reward to continuously reallocate resources between exploration and exploitation, while controlling population size, elite preservation, and restarts. The trained actor can operate in a supervision‑free inference mode, enabling fast deployment with maintained performance, and demonstrates superior convergence accuracy and robustness on benchmark optimization and structural health monitoring problems.

By Lechen Li (State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau 519000, China, College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China), Rongye Shi (School of Artificial Intelligence, Beihang University, Beijing 100191, China), Wanhuan Zhou (State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau 519000, China)