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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 large-scale 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.