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Portfolio selection involves optimizing simultaneously financial goals suchas risk, return and Sharpe ratio. This problem holds considerable importance ineconomics. However, little has been studied related to the nonconvexity of theobjectives. This paper proposes a novel generalized approach to solve thechallenging Portfolio Selection problem in an intuitionistic fuzzy environmentwhere the objectives are soft pseudoconvex functions, and the constraint set isconvex. Specifically, we utilize intuitionistic fuzzy theory and flexibleoptimization to transform the fuzzy pseudoconvex multicriteria vector into apseudoconvex programming problem that can be solved by recent gradient descentmethods. We demonstrate that our method can be applied broadly without specialforms on membership and nonmembership functions as in previous works.Computational experiments on real-world scenarios are reported to show theeffectiveness of our method.

Multicriteria Portfolio Selection with Intuitionistic Fuzzy Goals as a Pseudoconvex Vector Optimization