.png){kind=icon}
返回
Resource scheduling optimization for industrial operating system using deep reinforcement learning and WOA algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2024.124765.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Industrial operating system
Resource scheduling
Whale optimization algorithm
Deep reinforcement learning
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
.png){kind=icon}
An enhanced multi-objective grey wolf optimizer for service composition in cloud manufacturing面向云制造服务组合的改进多目标灰狼优化器
PREAI
分享收藏A reinforcement learning-based metaheuristic algorithm for solving global optimization problems一种求解全局优化问题的基于强化学习的元启发式算法
PREAI
分享收藏分享收藏Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏
查看更多内容