PARALLEL MULTIDIMENSIONAL SCALING USING GRID COMPUTING: ASSESSMENT OF PERFORMANCE

Abstract

Multidimensional scaling 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 has been constructed combining evolutionary global search with efficient local descent. A parallel version of the proposed optimization algorithm is implemented to enable solution of large scale problems in acceptable time. In the present paper we investigated the efficiency of the parallel version of the algorithm on PC clusters and computational grids.