Abstract
Solving high-dimensional structural model updating problems requires an algorithm capable of navigating complex, non-convex landscapes with correlated parameters. Existing hybrid evolutionary algorithms typically rely on static architectures or fixed switching rules, resulting in disjointed search phases. To address this, this study proposes a Deep Reinforcement Learning-governed dynamic DE-CMAES Orchestration (DRL-DCO) algorithm, in which a Deep Deterministic Policy Gradient (DDPG)-based actor-critic agent continuously governs the evolutionary process as a single, unified system rather than a mechanical concatenation of algorithms. Guided by a progression-aware state representation and a diversity-informed reward, the agent fluidly reallocates computational resources between the differencevector-based exploration of Differential Evolution (DE) and the covariance-guided exploitation of CMA-ES, while jointly regulating population size, elite preservation, and a restart mechanism to escape local optima. This allows DRL-DCO to autonomously transition between exploration-dominant, exploitation-dominant, and mixed-strategy regimes across generations. Beyond the training phase, the trained actor can operate in a supervision-free inference mode, where the internalized policy autonomously orchestrates DE and CMA-ES control from observed search states through forward inference alone, without critic evaluation or weight updates, enabling faster deployment while retaining full effectiveness. Validated on high-dimensional single-objective optimization benchmarks and the IASC-ASCE structural health monitoring benchmark, DRL-DCO achieves superior convergence accuracy and robustness compared to state-of-the-art adaptive and hybrid evolutionary algorithms, as well as single-operator DRL-governed baselines.
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Cite this article
APA 7
Li, L., Shi, R., & Zhou, W. (2026). Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating. https://omanscience.com/en/articles/learning-to-orchestrate-evolutionary-search-progression-aware-deep-reinforcement-learning-for-dynamic-de-cma-es-coordination-in-optimization-and-struc
MLA 9
Li, Lechen, et al. "Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating." https://omanscience.com/en/articles/learning-to-orchestrate-evolutionary-search-progression-aware-deep-reinforcement-learning-for-dynamic-de-cma-es-coordination-in-optimization-and-struc.
Chicago (author–date)
Li, Lechen, Rongye Shi, and Wanhuan Zhou. 2026. "Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating." https://omanscience.com/en/articles/learning-to-orchestrate-evolutionary-search-progression-aware-deep-reinforcement-learning-for-dynamic-de-cma-es-coordination-in-optimization-and-struc.
Harvard
Li, L., Shi, R. and Zhou, W. (2026) 'Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating', Available at: https://omanscience.com/en/articles/learning-to-orchestrate-evolutionary-search-progression-aware-deep-reinforcement-learning-for-dynamic-de-cma-es-coordination-in-optimization-and-struc.
Vancouver
Li L, Shi R, Zhou W. Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating. https://omanscience.com/en/articles/learning-to-orchestrate-evolutionary-search-progression-aware-deep-reinforcement-learning-for-dynamic-de-cma-es-coordination-in-optimization-and-struc
IEEE
L. Li, R. Shi, and W. Zhou, "Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating," https://omanscience.com/en/articles/learning-to-orchestrate-evolutionary-search-progression-aware-deep-reinforcement-learning-for-dynamic-de-cma-es-coordination-in-optimization-and-struc.