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

摘要

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.