Evaluating Incomplete DCOP Algorithms On Large-Scale Problems

Abstract

The distributed constraint optimization problem (DCOP) has emerged as one of the most promising coordination techniques in multi-agent systems (MAS). However, because DCOP is known to be NP-hard, the existing DCOP techniques are often unsuitable for large-scale applications, which require scalable algorithms to deal with severely limited computing and communication. Moreover, the selection of DCOP algorithm is a challenging and critical task for obtaining a desirable performance on certain MAS domains. In this paper, we present a performance analysis of incomplete DCOP algorithms on large-scale DCOPs. We experimentally evaluate the state-of-the-art incomplete algorithms on two types of problems involving hundreds of variables with different network topologies and densities. Such performance analysis can help to mitigate the challenges of selection of algorithm for a number of realistic large-scale, complex MAS applications.