Improving the Distributed Constraint Optimization Using Social Network Analysis

Distributed Constraint Optimization Problem (DCOP) has emerged as one of most important formalisms for distributed reasoning in multiagent systems. Nevertheless, there are few real world applications based on methods for solving DCOP, due to their inefficiency in some scenarios. This paper introduces the use of Social Network Analysis (SNA) techniques to improve the performance in pseudo-tree-based DCOP algorithms. We investigate when the SNA is useful and which techniques can be applied in some DCOP instances. To evaluate our proposal, we use the two most popular complete and optimal DCOP algorithms, named ADOPT and DPOP, and compare the obtained results with others wellknown pre-processing techniques. The experimental results show that SNA techniques can speed up ADOPT and DPOP algorithms.