Abstract
In evolutionary multi-objective optimization, the Pareto front is approximated using a set of representative candidate solutions with good convergence and diversity. However, most existing multi-objective evolutionary algorithms have general difficulty in the approximation of Pareto fronts with complicated geometries. To address this issue, we propose a generic front modeling method for evolutionary multi-objective optimization, where the shape of the nondominated front is estimated by training a generalized simplex model. On the basis of the estimated front, we further develop a multi-objective evolutionary algorithm, where both the mating selection and environmental selection are driven by the approximate non-dominated fronts modeled during the optimization process. For performance assessment, the proposed algorithm is compared with several state-of-the-art evolutionary algorithms on a wide range of benchmark problems with various types of Pareto fronts and different numbers of objectives. Experimental results demonstrate that the proposed algorithm performs consistently on a variety of multi-objective optimization problems.