An Approximate Algorithm for DCOP with Optimal Solution Attainment Rate of 0.99

Abstract

Distributed constraint optimization problems (DCOP) have attracted attention as a means of resolving distribution problems in multiagent environments. The authors has proposed a multiplex method targeting the improved efficiency of a distributed nondeterministic approximate algorithm for distributed constraint optimization problems. The multiplex method targeting the improved efficiency of a distributed nondeterministic approximate algorithm have been proposed for distributed constraint optimization problems. Since much of the computation time is used to transmit messages, improving efficiency using a multiplex computation of distributed approximate algorithms might be feasible, presuming that the computation time of each node or a small change in message length has no direct impact. Although it is usually impossible to guarantee that the approximation algorithm can obtain the optimal solution, the authors managed to do so, using a theoretically determined multiplex method. In addition, the authors shows the feasibility of an optimal solution attainment rate of 0.99 by an experiment using a Distributed Stochastic Search Algorithm.