An algorithm for approximating nondominated points of convex multiobjective optimization problems

‎In this paper‎, ‎we present an algorithm for generating approximate nondominated points of a multiobjective optimization problem (MOP)‎, ‎where the constraints and the objective functions are convex‎. ‎We provide outer and inner approximations of nondominated points and prove that inner approximations provide a set of approximate weakly nondominated points‎. ‎The proposed algorithm can be applied for differentiable or nondifferentiable convex MOPs‎. ‎To illustrate efficiency of the proposed algorithm for convex MOPs‎, ‎we provide numerical examples.