Resource scheduling optimization for industrial operating system using deep reinforcement learning and WOA algorithm
Ting Shu, Zhijie Pan, Zuohua Ding, Zhangqing Zu
Abstract
Industrial operating systems (IOS) are essential for supporting smart manufacturing, particularly in managing and utilizing heterogeneous production resources through resource instantiation scheduling (RIS) technique. However, RIS faces the challenge of efficiently selecting optimal resource service compositions from numerous options with varying quality of service . To boost the solving of the RIS problem and improve the quality of the solution, this paper proposes a novel hybrid algorithm, named DWOA, based on the whale optimization algorithm (WOA) and deep reinforcement learning (DRL). It first incorporates the DRL algorithm to learn experience from the historical data regarding exploration and exploitation in the WOA search process and train an optimal behavior decision model. Subsequently, utilizing the trained model, the DWOA can effectively guide the search agent in achieving a better balance between global exploration and local exploitation, thereby enhancing its convergence speed and solution quality. The effectiveness and efficiency of the DWOA approach are evaluated by the CEC2017 benchmark functions and RIS problems with various scales, compared with 11 state-of-the-art methods. The experimental results indicate that our method converges faster and produces better solutions for RIS problems.