基于自适应DRL与秃鹰搜索融合的光储系统多时间尺度优化策略OA

This article proposes a multi time scale optimization strategy for optical storage systems based on the fusion of adaptive deep reinforcement learning(DRL)and multimodal vulture search(MVS),in order to improve the energy utilization efficiency of the optical storage system during operation,reduce overall operating costs,and enhance the stability and adaptability of the system in complex and changing environments.The proposed multi time scale optimization strategy for optical storage systems proposes a dynamic weight allocation mechanism to adaptively adjust the decision weights of deep reinforcement learning and multimodal vulture search algorithms.At the same time,this method introduces a cross validation mechanism to achieve mutual optimization of parameters and strategies between the two algorithms,thereby achieving optimal decisions at different time scales and system states.In addition,the multi time scale optimization strategy of the optical storage system integrates knowledge transfer technology,enabling long-term planning knowledge to guide short-term decisions while ensuring global optimization and improving the real-time performance of the algorithm.The experimental results show that the multi time scale optimization strategy for optical storage systems is superior to existing methods in terms of optimization effectiveness,computational efficiency,and system adaptability,providing new theoretical basis and practical guidance for the intelligent and efficient operation of optical storage collaborative systems.