Evaluating the performance of DCOP algorithms in a real world, dynamic problem

Complete algorithms have been proposed to solve problems modelled as distributed constraint optimization (DCOP). However, there are only few attempts to address real world scenarios using this formalism, mainly because of the complexity associated with those algorithms. In the present work we compare three complete algorithms for DCOP, aiming at studying how they perform in complex and dynamic scenarios of increasing sizes. In order to assess their performance we measure not only standard quantities such as number of cycles to arrive to a solution, size and quantity of exchanged messages, but also computing time and quality of the solution which is related to the particular domain we use. This study can shed light in the issues of how the algorithms perform when applied to problems other than those reported in the literature (graph coloring, meeting scheduling, and distributed sensor network).