A Hybrid Multiobjective Bat Algorithm for Fuzzy Portfolio Optimization with Real-World Constraints

Abstract

This paper investigates a fuzzy portfolio selection problem in the framework of multiobjective optimization. A multiobjective mean–semivariance–entropy model with fuzzy returns is proposed for portfolio selection. Specifically, it simultaneously optimizes the return, risk and portfolio diversification, taking into account transaction costs, liquidity, buy-in thresholds, and cardinality constraints. Since this kind of mixed-integer nonlinear programming problems cannot be efficiently solved by the conventional optimization approaches, a new metaheuristic method termed as the hybrid BA–DE is developed by combining features of the bat algorithm (BA) and differential evolution (DE). In order to demonstrate the effectiveness of the proposed approaches, we also provide a numerical example.