@article{Yang2025, 
author = {Jia-Lin Yang and Hao-Ran Sun and Chai-Rui Chen and Ruo-Bin Wang and Lin Xu and Jeng-Shyang Pan and Shu-Chuan Chu},
title = {Reinforcement learning-guided Animated Oat Optimization Algorithm with dynamic niching for high-dimensional optimization problems},
year = {2025},
journal = {Electronic Research Archive},
volume = {33},
number = {9},
pages = {5536-5590},
keywords = {Animated Oat Optimization Algorithm, reinforcement learning, dynamic niching, multi-armed bandit, high-dimensional optimization},
url = {https://www.sciopen.com/article/10.3934/era.2025248},
doi = {10.3934/era.2025248},
abstract = {High-dimensional optimization problems face challenges from exponentially expanding search spaces and deceptive local optima resisting metaheuristics. In order to address these issues, a reinforcement learning-guided Animated Oat Optimization Algorithm with a dynamic niching strategy, called RLDN-AOO, is proposed in this research. RLDN-AOO offers the following major novelties: i) a mathematically formulated three-state dynamic niching mechanism that adaptively partitions the population, preserves diversity, and enhances the algorithm's ability to escape local optima, and ii) a reinforcement learning strategy selection mechanism is proposed to address the issue of the algorithm's inadequate dynamic adaptability. We compared it with state-of-the-art algorithms (CEC2017, Dim = 50, 100, 200, 500), including LSHADE-SPACMA, CMA-ES variants, and RL-based optimizers. In addition, we applied it in the optimization of BP neural networks. Experimental results showed that RLDN-AOO achieves competitive performance across most benchmarks and, in some cases, performs comparably to LSHADE-SPACMA variants. The source code of RLDN-AOO is openly accessible via https://github.com/robingit77/RLDN-AOO.}
}