Improving The Optimal Solution Attainment Rate by Multiplexing Method

Distributed constraint optimization problems (DCOP) have attracted attention as a means of resolving distribution problems in multiagent environments. We [1] proposed a multiplex method target ing the improved effic iency of a d ist r ibu ted nondeterministic approximation algorithm for distributed constraint optimization problems. The multiplex method target ing the improved effic iency of a d ist r ibu ted nondeterministic approximation algorithm has 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 approximation 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, we managed to do so, using a theoretically determined multiplex method. In addition, we show the feasibility of an optimal solution attainment rate of 0.99 by an experiment using a Distributed Stochastic Search Algorithm.