An Artificial Bee Colony Based Algorithm for Continuous Distributed Constraint Optimization Problems

Abstract: Distributed Constraint Optimization Problems (DCOPs) are a frequently used framework in which a set of independent agents choose values from their respective discrete domains to maximize their utility. Although this formulation is typically appropriate, there are a number of real-world applications in which the decision variables are continuous-valued and the constraints are represented in functional form. To address this, Continuous Distributed Constraint Optimization Problems (C-DCOPs), an extension of the DCOPs paradigm, have recently grown the interest of the multi-agent systems field. To date, among different approaches, population-based algorithms are shown to be most effective for solving C-DCOPs. Considering the potential of population-based approaches, we propose a new C-DCOPs solver inspired by a well-known population-based algorithm Artificial Bee Colony (ABC). Additionally, we provide a new exploration method that aids in the further improvement of the algorithm's solution quality. Finally, We theoretically prove that our approach is an anytime algorithm and empirically show it produces significantly better results than the state-of-the-art C-DCOPs algorithms.