JFET_V3_N1_RP4 B.V. Babu Ashish M. Gujarathi Journal on Future Engineering and Technology 2230 – 7184 3 1 47 59 Multi objective optimization, Multi Objective Differential Evolution (MODE), Pareto optimal front, Evolutionary computation, Population based search algorithms Multi-Objective Differential Evolution (MODE), a multi-population, multi-objective optimization approach using Differential Evolution (DE) has been successfully applied to selected real world problems. This algorithm is equipped with non-dominated population selection combined with basic DE algorithm. In this study, the MODE algorithm is further applied on six different Test problems with/without constraints and extensive simulation runs are carried out for parametric study. Pareto optimal solutions are obtained for each test problems. The Pareto fronts are compared on the basis of various values of key MODE parameters. This work resulted in identifying the sensitivity of various key parameters of the MODE algorithm applied on the hard test problems. August - October 2007 Copyright © 2007 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=697