BnB-ADOPT+ with Several Soft AC Levels

Distributed constraint optimization problems can be solved by BnBADOPT$^+$, a distributed asynchronous search algorithm. In the centralized case, local consistency techniques applied to constraint optimization have been shown very beneficial to increase performance. In this paper, we combine BnB-ADOPT$^+$ with different levels of soft arc consistency, propagating unconditional deletions caused by either the enforced local consistency or by distributed search. The new algorithm maintains BnB-ADOPT$^+$ optimality and termination. In practice, this approach decreases substantially BnB-ADOPT$^+$ requirements in communication cost and computation effort when solving commonly used benchmarks. Source URL: https://www.iiia.csic.es/en/node/54765 Links [1] https://www.iiia.csic.es/en/staff/patricia-gutierrez [2] https://www.iiia.csic.es/en/staff/pedro-meseguer [3] https://www.iiia.csic.es/en/bibliography?f[keyword]=912 [4] https://www.iiia.csic.es/en/bibliography?f[keyword]=911 [5] https://www.iiia.csic.es/en/bibliography?f[keyword]=825