A Multiobjective Evolutionary Algorithm - The Study Cases

In the few last years, among other tools a multiobjective evolutionary algorithm (MOBEA) for successfully solving many mul-ticriteria 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 diicult 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 brieey discussing a concept for our approach we illustrate its eeectiveness for solving some diicult MOPs and propose some basic way to improve it in the future.