This paper represents the second part of a study concerning the so–called G–multiobjective programming. A new approach to duality in differentiable vector optimization problems is presented. The techniques used are based on the results established in the paper: On G–invex multiobjective programming. Part I. Optimality by T.Antczak. In this work, we use a generalization of convexity, namely G–invexity, to prove new duality results for nonlinear differentiable multiobjective programming problems. For such vector optimization problems, a number of new vector duality problems is introduced. The so–called G–Mond–Weir, G–Wolfe and G–mixed dual vector problems to the primal one are defined. Furthermore, various so–called G–duality theorems are proved between the considered differentiable multiobjective programming problem and its nonconvex vector G–dual problems. Some previous duality results for differentiable multiobjective programming problems turn out to be special cases of the results described in the paper.
