Parallel hybrid algorithm for global optimization of problems occurring in MDS-based visualization

Multidimensional scaling is a widely used technique for visualization of multidimensional data. For the implementation of a multidimensional scaling technique a difficult global optimization problem should be solved. To attack such problems a hybrid global optimization method is developed combining evolutionary global search with local descent. A parallel version of the proposed method is implemented to enable solution of large scale problems in acceptable time. The results of the experimental investigation of the efficiency of the proposed method are presented.