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Distributed Pseudo-tree Optimization Procedure (DPOP) is a well-known messagepassing algorithm that has been used to provide optimal solutions ofDistributed Constraint Optimization Problems (DCOPs) -- a framework that isdesigned to optimize constraints in cooperative multi-agent systems. Thetraditional DCOP formulation does not consider those constraints that must besatisfied (also known as hard constraints), rather it concentrates only on softconstraints. However, the presence of both types of constraints are observed ina number of applications, such as Distributed Radio Link Frequency Assignmentand Distributed Event Scheduling, etc. Although the combination of these typesof constraints is recently incorporated in DPOP to solve DCOPs, scalabilityremains an issue for them as finding an optimal solution is NP-hard.Additionally, in DPOP, the agents are arranged as a DFS pseudo-tree. Recentlyit has been observed that the constructed pseudo-trees in this way often cometo be chain-like and greatly impair the algorithm's performance. To addressthese issues, we develop an algorithm that speeds up the DPOP algorithm byreducing the size of the messages exchanged and increasing parallelism in thepseudo tree. Our empirical evidence suggests that our approach outperforms thestate-of-the-art algorithms by a significant margin.

Speeding Up Distributed Pseudo-tree Optimization Procedure with Cross Edge Consistency to Solve DCOPs