A local cost simulation-based algorithm to solve distributed constraint optimization problems.

ABSTRACT: As an important incomplete algorithm for solving Distributed Constraint Optimization Problems (DCOPs), local search algorithms exhibit the advantages of flexibility, high efficiency and high fault tolerance. However, the significant historical values of agents that affect the local cost and global cost are never taken into in existing incomplete algorithms. In this article, a novel Local Cost Simulation-based Algorithm named LCS is presented to exploit the potential of historical values of agents to further enhance the exploration ability of the local search algorithm. In LCS, the Exponential Weighted Moving Average (EWMA) is introduced to simulate the local cost to generate the selection probability of each value. Moreover, populations are constructed for each agent to increase the times

SUBMITTER: Shi M 

PROVIDER: S-EPMC10280401 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature