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Population based ACO algorithms for dynamic optimization problems are studied in this paper. In the population based approach a set of solutions is transferred from one iteration of the algorithm to the next instead of transferring pheromone information as in most ACO algorithms. The set of solutions is then used to compute the pheromone information for the ants of the next iteration. The population based approach can be used to solve dynamic optimization problems when a good solution of the old instance can be modified after a change of the problem instance so that it represents a reasonable solution for the new problem instance. This is tested experimentally for a dynamic TSP and dynamic QAP problem. Moreover the behavior of different strategies for updating the population of solutions are compared.