Multidimensional scaling (MDS) is a technique for visualization and exploratory analysis of multidimensional data aiming to discover a structure of sets of objects using information on similarities/dissimilarities between those objects. A difficult global optimization problem should be solved to minimize the error of visualization. A hybrid optimization algorithm combining evolutionary global search with efficient local descent has been constructed. A parallel version of the proposed optimization algorithm for multidimensional scaling will be implemented to enable solution of large scale problems in acceptable time. |
