Nature Inspired Intelligence for the Constrained Portfolio Optimization Problem
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
PSO-based possibilistc mean-variance model with transaction costs
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A portfolio model with quadratic subsection concave transaction costs based on PSO
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Constrained Portfolio Selection using Particle Swarm Optimization
Expert Systems with Applications: An International Journal
A hybrid algorithm for constrained portfolio selection problems
Applied Intelligence
| Hi-index | 0.00 |
In this paper, the realistic portfolio selection problem is studied and an algorithm named improved particle swarm optimization (IPSO) is presented to solve this problem. At first, a realistic portfolio selection model, as an alternative to the standard Markowitz model, is formulated for selecting portfolios with transaction costs and quantity constraint. In addition, due to these complex constraints traditional optimization algorithms fail to work efficiently and heuristic algorithms can be the best method, so we present an improved particle swarm optimization to solve our problem. Finally, a numerical example is given to illustrate our proposed effective model and method. Experiment results show that our proposed method is an efficient method for solving realistic portfolio selection problem and more superior than standard PSO method.