In order to solve the problem that it is difficult to get out of the local optimum to achieve further optimization in large-scale dense distributed constraint optimization problems(DCOP), this paper proposed a local parallel search framework(LPOS) for solving algorithms of DCOP. In LPOS, the agent searched all the value assignments of its local neighbors in parallel according to its current value assignment to further expand the search of solution space, so as to avoid the algorithm falling into local optimum prematurely. In order to ensure the convergence of the algorithm, this paper designed an adaptive equilibrium factor K to balance the exploration and exploitation ability of the DCOP solving algorithms. At the theoretical analysis, it proved that the LPOS could expand the search of solution space and the K could achieve the balance. The experimental results demonstrate that the performance of the proposed LPOS is better than the-state-of-the-art algorithms in both low density and high density. Specifically, LPOS significantly superior to the competitors when solving high density DCOP.

[2022-03-31] Accepted Paper /doc/2022.02.0035/v1

[2022-08-05] Printed Article /doc/2022.02.0035/v2