IMAGE SPACE ANALYSIS FOR UNCERTAIN MULTIOBJECTIVE OPTIMIZATION PROBLEMS: ROBUST OPTIMALITY CONDITIONS

We introduce the C-robust efficient solution and optimistic C-robust efficient solution of uncertain multiobjective optimization problems (UMOP). By using image space analysis, robust optimality conditions as well as saddle point sufficient optimality conditions for uncertain multiobjective optimization problems are established based on real-valued linear (regular) weak separation function and real-valued (vector-valued) nonlinear (regular) weak separation functions. We also introduce two inclusion problems by using the image sets of robust counterpart of (UMOP) and establish the relations between the solu-tion of the inclusion problems and the C-robust efficient solution (respectively, optimistic C-robust efficient solution) of (UMOP).

Automated summary

The study introduces the establishment of C-robust efficient solutions and optimistic C-robust efficient solutions for uncertain multiobjective optimization problems, using image space analysis and uncertainty factors to apply robust optimality conditions and saddle point sufficient optimality conditions to problem solving.