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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. When these problems are situated in a distributed setting, distributed algorithms are preferred or even necessary. 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 identify a category of problems where DynCOAA outperforms the other two algorithms, while it remains competitive for other categories of problems