A Local Search Based Approach to Solve Continuous DCOPs

Distributed Constraint Optimization Problems (DCOPs) are a suitable formulation for coordinating interactions (i.e. constraints) in cooperative multi-agent systems. The traditional DCOP model assumes that variables owned by the agents can take only discrete values and constraints' cost functions are defined for every possible value assignment of a set of variables. While this formulation is often reasonable, there are many applications where the decision variables are continuous-valued and constraints are in functional form. To overcome this limitation, Continuous DCOPs (C-DCOPs), an extension of the DCOPs model has been proposed that is able to formulate problems having continuous variables. The existing methods for solving C-DCOPs come with a huge computation and communication overhead. In this paper, we apply continuous nonlinear optimization methods on Cooperative Constraint Approximation (CoCoA) algorithm, which is a non-iterative, fast incomplete local search approach for solving DCOPs. We empirically show that our algorithm is able to provide high-quality solutions at the expense of smaller communication cost and execution time compared to the state-of-the-art C-DCOP algorithms.