An End-to-End Multiple Hyper-parameters Prediction Method for Distributed Constraint Optimization Problem

Distributed Constraint Optimization Problem (DCOP) is an important model for multi-agents, has been widely used in various fields. When a large scale of DCOP implement on the supercomputer, various parameters need to choose, and the complement time vary widely for different combinations of parameters. Automatically provided accurate operating parameters for DCOP can improve the operation speed and enables the rational use of computational resources. However, the number of hyper-parameters of DCOP is huge, and correlation exists between hyper-parameters, thus make the prediction of multiply hyper-parameters difficult. In this paper we propose a new framework combine graph neural network and recurrent neural network. The performance shows that our framework can outperform the SODA method.