This paper introduces a new topology and a framework for distributed constraint optimization approaches to solving complex problems. Initial experiments with this approach show decreasing communication overhead, high scalability and low execution times due to parallelization of tasks. In the experiments section we analyze a dry random choice algorithm run of the framework and measure the overhead time, then we compare the topology performance with a two level arborescent approach to distributed constraint optimization. The results show a significant improvement on execution time and better scalability.
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