Journal
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
Volume 65, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2019.102687
Keywords
Service composition; Deep reinforcement learning; QoS; Behavior strategy
Funding
- National Natural Science Foundation of China [61773120, 61772225]
- scientific project of mining machinery control and parts engineering center in Jiangsu province [JYAPT17-05]
- Xuzhou science and technology project [KH17003]
- Youth project of science and Technology of Department of Education in Hebei provincial [QN2016237]
- National Natural Science Foundation of Guangdong [2018B030311046]
- Guangdong University Key Platforms and Research Projects [2018KZDXM066, 2017KZDXM081, 2015KQNCX153]
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Service composition is a research hotspot with practical value. With the development of Web service, many Web services with the same functional attributes emerge. However, service composition optimization is still a big challenge since the complex and unstable composition environment. To solve this problem, we propose an adaptive service composition based on deep reinforcement learning, where recurrent neural network (RNN) is utilized for predicting the objective function, improving its expression and generalization ability, and effectively solving the shortcomings of traditional reinforcement learning in the face of large-scale or continuous state space problems. We leverage heuristic behavior selection strategy to divide the state set into hidden state and fully visible state. Effective simulation of hidden state space and fully visible state of the evaluation function can further improve the accuracy and efficiency of the combined results. We conduct comprehensive experiment and experimental results have shown the effectiveness of our method. (C) 2019 Published by Elsevier Inc.
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