Using Collective Behavior of Coupled Oscillators for Solving DCOP

The distributed constraint optimization problem (DCOP) has emerged as one of the most promising coordination techniques in multiagent systems. However, because DCOP is known to be NP-hard, the existing DCOP techniques are often unsuitable for largescale applications, which require distributed and scalable algorithms to deal with severely limited computing and communication. In this paper, we present a novel approach to provide approximate solutions for large-scale, complex DCOPs. This approach introduces concepts of synchronization of coupled oscillators for speeding up the convergence process towards high-quality solutions. We propose a new anytime local search DCOP algorithm, called Coupled Oscillator OPTimization (COOPT), which amounts to iteratively solving a DCOP by agents exchanging local information that brings them to a consensus. We empirically evaluate COOPT on constraint networks involving hundreds of variables with different topologies, domains, and densities. Our experimental results demonstrate that COOPT outperforms other incomplete state-of-the-art DCOP algorithms, especially in terms of the agents’ communication cost and solution quality.