An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection

Abstract

Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.

Portfolio rebalancing under uncertainty using meta-heuristic algorithm

“…In particular, Yan and Huang [15,18] applied Liu's uncertainty theory to portfolio selection problem using uncertain variables when securities returns are neither random nor fuzzy. Moreover, in order to solve uncertain portfolio optimization model, Zhang et al and Chen proposed meta-heuristic algorithms [19,20]. Yet there aren't too many studies in the literature on portfolio rebalancing under uncertainty using experts' subjective evaluations.…”

A General Technical Route for Parameter Optimization of Ship Motion Controller Based on Artificial Bee Colony Algorithm

“…Glance at ABC Algorithm Introduced by D. Karaboga [6]- [9], the ABC algorithm is a nature-inspired algorithm based on the intelligent foraging behavior of honey bee swarm and has been used to find an optimal solution in numeric optimization problems [10]. Based on many benchmark functions, researches [7]- [9] showed the ABC algorithm was competitive to other population-based algorithms, such as GA, Particle swarm optimization (PSO), Differential Evolution (DE), evolution strategies and Particle Swarm inspired Evolutionary Algorithm (PS-EA), etc., with an advantage of employing fewer control parameters [11]. Since its invention in 2005, ABC algorithm has attracted a lot of attention and been applied to solve many kinds of problem beside numerical function optimization [10].…”

“…1) The concrete content of TF block calculating ship motion was given by converting the following differential equation (11) according to [13], [14].…”