Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

• A possibilistic semi-absolute deviation model with real-world constraints is proposed.
• A modified artificial bee colony (MABC) algorithm is developed to solve the proposed model.
• Real-world constraints have great influence on optimal strategies making.
• MABC algorithm outperforms several heuristic algorithms.

In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.