This paper aims to establish first-order necessary optimality conditions for non-smooth multi-objective generalized semi-infinite programming problems‎. ‎These problems involve inequality constraints whose index set depends on the decision vector‎, ‎and all emerging functions are assumed to be locally Lipschitz‎. ‎We introduce a new constraint qualification for these problems‎. ‎Building upon this qualification‎, ‎we derive an upper estimate for the Clarke sub-differential of the value function of the problem‎. ‎Furthermore‎, ‎we demonstrate the necessary optimality conditions for Properly efficient solutions to the problem‎.

This paper studies the convex multiobjective optimization problem with vanishing constraints. We introduce a new constraint qualification for these problems, and then a necessary optimality condition for Properly efficient solutions is presented. Finally by imposing some assumptions, we show that our necessary condition is also sufficient for proper efficiency. Our results are formulated in terms of convex subdifferential.

Trade-off between objective functions in multi-objective optimization is one of the tools for interpreting and studying efficient solutions. Properly efficient solutions are one of the most important theoretical and practical concepts that represent the behavior of the objective functions during a process change. Actually, these solutions are those efficient solutions that filter the anomalies of objective functions at some points, and this will help the manager to decision making to choose more important solutions. One of the most important tools for obtaining solutions with bounded trade-off in multi-objective optimization field is the Sum weighted scalarization method, which many authors have been studying it in interactive optimization field. This paper provides a method for obtaining Properly efficient solutions near the ideal point with a theoretical and interactive view and using Sum weighted scalarization method. Since being near to ideal point will be abele to a preference of decision maker; this method examines the preferences of the decision maker without sacrifice the theory. Therefore, this paper presents an approach to finding Properly efficient solutions near to the ideal point.

In the presented paper, we investigate efficient solutions to optimization problems with multiple criteria and bounded trade-offs. A nonlinear optimization problem to find the relationships between the upper bound for trade-offs and objective functions is presented. Due to this problem, we determine some Properly efficient points that are closer to the ideal point. To this end,  we apply the extended form of the generalized Tchebycheff norm. Note that all the presented results work for general problems and no convexity assumption is needed.

In this paper, a modified scalarization technique for finding Pareto optimal points of multiobjective optimization problems is provided. The proposed method is a combination of the normal constraint and elastic constraint method. First, we introduce the optimization problem of the modified method and then we present an algorithm for obtaining the set of Pareto points. Thereafter, the relationship between optimal solutions of this scalarization problem and (weakly, Properly) efficient solutions of the multiobjective optimization problems are analyzed. Indeed, some necessary conditions for (weak, proper) efficiency are presented. All the provided results are established without any convexity assumption. Furthermore, we propose a new algorithm for approximating the Pareto front of multiobjective optimization problems. We solve some test problems by applying the suggested algorithm and compare the results with some existing methods, including the epsilon-constraint method, the Pascolleti-Serafini method and the NBI method. The obtained results highlight the efficiency of our approach in comparison with examined methods.

In this paper, we focus on the utilization of the feasible value constraint technique to address multiobjective optimization problems (MOPs). It is attempted to overcome certain drawbacks associated with this method, such as restrictions on functions and weights, inflexibility in constraints, and challenges in assessing proper efficiency. To accomplish this, we propose an improved version of the feasible value constraint technique. Then, by incorporating approximate solutions, we establish connections between $\varepsilon$-(weakly, Properly) efficient points in a general MOP and $\epsilon$-optimal solutions to the scalarization problem.