SOLVING DISTRIBUTED CONSTRAINT OPTIMIZATION PROBLEMS USING ANT COLONY OPTIMIZATION

As one of the top-performing metaheuristics, ant colony optimization (ACO) has been successfully applied to a wide range of optimization problems. This paper proposes ACO-DCOP, an novel incomplete distributed algorithm, which exploits ACO's powerful global search ability to solve distributed constraint optimization problems. Specifically, ACO is redesigned with message passing mechanism in distributed settings. To evaluate the performance of the proposed algorithm, we compare it with several state-of-the-art DCOP algorithms The experimental results demonstrate the superiority of ACO-DCOP over other algorithms in both solution quality and simulated time.