Title:Global descent method for non-convex multi-objective optimization problems
Authors : Bikram Adhikary, Md Abu Talhamainuddin Ansary, Savin Treanţă

Journal: Numerical Algorithms

Abstract: In this paper, we develop a global descent method for non-convex multi-objective optimization problems. The proposed approach builds upon foundational concepts from single-objective global descent techniques while removing the need for pre- defined scalars or ordering information of objective functions. Initially, the pro- posed method identifies a local weak efficient solution using any suitable descent algorithm, then applies an auxiliary function termed the multi-objective global de- scent function to systematically transition toward improved local weak efficient solutions. It is justified that this method can generate a global Pareto front for non- convex problems, which has many different local Pareto fronts. Finally, comprehen- sive numerical experiments on benchmark non-convex multi-objective optimization problems have been done to demonstrate the method’s robustness, scalability and effectiveness of the proposed method.