Comparing two approaches to dynamic, distributed constraint satisfaction
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Benchmarking and solving dynamic constrained problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
The power of ants in solving Distributed Constraint Satisfaction Problems
Applied Soft Computing
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Numerous problems in software coordination, operations research, manufacturing control and others can be transformed in constraint optimization problems (COPs). Moreover, most practical problems change constantly, requiring algorithms that can handle dynamic problems.In this paper we present the dynamic constraint optimization ant algorithm (DynCOAA) that can solve dynamic COPs. DynCOAA is specifically designed for dynamic problems, as it is based upon the ant colony optimization (ACO) meta-heuristic that has already proven its merit in other dynamic optimization problems. DynCOAA is a distributed algorithm that is suited for a one-on-one mapping between variables and hosts, but it can effectively accommodate multiple variables per host. We compared our algorithm to two existing algorithms for dynamic constraint optimization: DynAWC and DynDBA. We find that DynCOAA outperforms DynAWC and DynDBA on some problems, while remaining competitive on others.