Numerous organizations collect and distribute non-aggregate personal data for a variety of different purposes, including demographic and public health research. In these situations, the data distributor is responsible with the protection of the anonymity and personal information of individuals. Microaggregation is one of the most commonly used statistical disclosure control methods. In microaggregation, the set of original records is first partitioned into several groups. The records in the same group are similar to each other. The minimum number of records in each group is k. Each record is replaced by the mean value of the group (centroid). The confidentiality of records is protected by ensuring that each group has at least a minimum of k records and each record is indistinguishable from at least k-1 other records in the microaggregated dataset. The goal of this process is to keep the within-group homogeneity higher and the information loss lower, where information loss is the sum squared deviation between the actual records and the group centroids. Several heuristics have been proposed for the NP-hard minimum information loss microaggregation problem. Among the most promising methods is the multivariate Hansen-Mukherjee (MHM) algorithm that uses a shortest path algorithm to identify the best partition consistent with a specified ordering of records. Developing improved heuristics for ordering multivariate points for microaggregation remains an open research challenge. This dissertation adapts a version of the population-based ant colony optimization algorithm (PACO) to order records within which MHM algorithm is used iteratively to improve the quality of grouping. Results of computational experiments using benchmark test problems indicate that P-ACO/MHM based microaggregation algorithm yields comparable or improved information loss than those obtained by extant methods.