A Novel Hybrid ICA-FA Algorithm for Multiperiod Uncertain Portfolio Optimization Model Based on Multiple Criteria

This paper deals with a multiperiod portfolio selection problem in an uncertain investment environment, in which the returns of securities are assumed to be uncertain variables and determined by experts' subjective evaluation. Based on uncertain theory, we present a novel multiperiod multiobjective mean-variance-skewness model by considering multiple realistic investment constraints such as transaction cost, hounds on holdings, cardinality, etc. For the proposed solution, we first apply a weighted max-min fuzzy goal programming approach to convert the proposed multiobjective programming model into a single-objective one. After that, we design a novel hybrid of an imperialist competitive algorithm (ICA) and a firefly algorithm (FA), termed ICA-FA, to solve it. Finally, we provide a numerical example to demonstrate the effectiveness of the proposed model and corresponding algorithm.