Multidimensional scaling with multiswarming

We introduce a new method for multidimensional scaling in dissimilarity data that is based on preservation of metric topology between the original and derived data sets. The model seeks neighbors in the derived data that have the same ranks as in the input data. The algorithm we use to optimize the model is a modification of particle swarm optimization called multiswarming. We compare the new method to three well known approaches: Principal component analysis, Sammon´s method, and (Kruskal´s) metric MDS. Our method produces feature vector realizations that compare favorably with the other approaches on three real relational data sets.