A NEW HYBRIDIZATION FOR IMPROVING THE CONVERGENCE OF THE MOMA-PLUS METHOD

Abstract

We propose in this article a hybridization of the algorithm of the MOMA-Plus method and that of the Differential Evolution method. This hybridization consists of defining a simplex around an efficient solution generated by MOMA-plus and applying the Differential Evolution algorithm to find a better solution than that obtained by MOMA-plus. The results interpreted through a performance study of the solutions obtained on multiobjective optimization test problems show that this hybridization improves the convergence of the basic MOMA-plus algorithm. Moreover, a better complexity than that of basic MOMA-plus is obtained.