In the provision of dynamic data-intensive services, the cost and response time of data sets as well as the states of services may change over time. An ant colony system for this problem is studied in this paper. Specifically, we consider changing the QoS attributes of services and replacing a certain number of services with new ones at different frequencies. In order to adapt the ant colony system to handle the dynamic scenarios, several pheromone modification strategies in reaction to changes of the optimization scenarios are investigated. The aim of the strategies is to find a balance between preserving enough old pheromone information to speed up the search process, and resetting enough new pheromone information to facilitate the ants to find a new solution for the changed scenarios. The strategies differ in their degree of reinitialized pheromone values with respect to the information that has been used to decide the amount of pheromone values. Moreover, the behaviors of different strategies for modifying pheromone information are compared.