A Multiobjective Evolutionary Algorithm The Study Cases

In the few last years, among other tools a multiobjective evolutionary algorithm (MOBEA) for successfully solving many multicriteria optimization problems (MOPs) was proposed. However, there is a lack of the systematically testing our approach with other benchmark MOPs that may cause the algorithm very difficult to achieve its performance: robustness of the convergence to the true pareto-optimal surface; uniform distribution of the population on it. In this work after briefly discussing a concept for our approach we illustrate its effectiveness for solving some difficult MOPs and propose some basic way to improve it in the future. 1 INTRODUCTION The MOBEA system is implemented for solving the following MOPs with linear and non-linear constraints: min ~x ~ f(~x) = min ~x (f 1 (~x); f 2 (~x); \Delta \Delta \Delta ; fN (~x)) where ~x = (x 1 ; x 2 ; \Delta \Delta \Delta ; x n ) T 2 F ` S ` R n ; N and n are the number of objective functions and the number of varia..