JACIII Vol.15 No.4 pp. 473-478
doi: 10.20965/jaciii.2011.p0473


A Hybrid Particle Swarm Optimization Approach and its Application to Solving Portfolio Selection Problems

Shamshul Bahar Yaakob*,** and Junzo Watada*

*Graduate School of IPS, Waseda University, 2-7 Hibikino, Wakamatsu, Kitakyushu 808-0135, Japan

**School of Electrical Systems Engineering, Universiti Malaysia Perlis, 02600 Perlis Malaysia

January 7, 2011
March 3, 2011
June 20, 2011
particle swarm optimization, hybrid particle swarm optimization, modern portfolio theory, genetic algorithm
In modern portfolio theory, the basic topic is how to construct a diversified portfolio of financial securities to improve trade-offs between risk and return. The objective of this paper is to apply a heuristic algorithm using Particle Swarm Optimization (PSO) to the portfolio selection problem. PSO makes the search algorithm efficient by combining a local search method through self-experience with the global search method through neighboring experience. PSO attempts to balance the exploration-exploitation tradeoff that achieves efficiency and accuracy of optimization. In this paper, a newly obtained approach is proposed by making simple modifications to the standard PSO: the velocity is controlled and the mutation operator of Genetic Algorithms (GA) is added to solve a stagnation problem. Our adaptation and implementation of the PSO search strategy are applied to portfolio selection. Results of typical applications demonstrate that the Velocity Control Hybrid PSO (VC-HPSO) proposed in this study effectively finds optimum solution to portfolio selection problems. Results also show that our proposed method is a viable approach to portfolio selection.
Cite this article as:
S. Yaakob and J. Watada, “A Hybrid Particle Swarm Optimization Approach and its Application to Solving Portfolio Selection Problems,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.4, pp. 473-478, 2011.
Data files:
  1. [1] H. Markowitz, “Portfolio selection,” J. of Finance, Vol.7, No.1, pp. 77-91, 1952.
  2. [2] D. G. Luenberger, “Investment sciences,” Oxford University press, New York, pp. 137-162 and 481-483, 1998.
  3. [3] J. Board and C. Sutcliffe, “The application of operation research technique to financial markets,” Stochastic programming e-print series, 1999.
  4. [4] A. F. Perold, “Large-scale portfolio optimization,” Management Sciences, Vol.30, No.10, pp. 1143-1160, 1984.
  5. [5] F. J. Jones, “An overview of institutional fixed income strategies,” Professional perspectives on fixed income portfolio management, John Wiley & Sons, pp. 1-13, 2000.
  6. [6] T.-J. Chang, N.Meade, E. Beasley, and M. Sharaiha, “Heuristics for cardinality constrained portfolio optimization,” Computer & Operational Research, Vol.27, No.13, pp. 1271-1302, 2000.
  7. [7] A. Schaerf, “Local search techniques for constrained portfolio selection problems,” Computational Economics, Vol.20, No.3, pp. 177-190, 2002.
  8. [8] Y. Crama and M. Schyns, “Simulated annealing for complex portfolio selection problems,” European J. of Operational Research, Vol.150, No.3, pp. 546-571, 2003.
  9. [9] S. B. Yaakob and J. Watada, “Maintenance of a traffic signal lights based on portfolio model,” Int. J. of Simulation Systems, Science & Technology, Vol.9, No.2, pp. 11-22, 2008.
  10. [10] A. Fernandez and S. Gomez, “Portfolio selection using neural networks, Computers & Operations Research,” Vol.34, No.4, pp. 1177-1191, 2007.
  11. [11] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” Proc. of the IEEE Int. Conf. on Neural Network, pp. 1942-1948, 1995.
  12. [12] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” Proc. 6th Int. Symposium on Micro Machine and Human Science, pp. 39-43, 1995.
  13. [13] A. Salman, I. Ahmad, and S. Al-Madani, “Particle swarm optimization for task assignment problem,” J. of Microprocessors and Microsystems, Vol.26, No.8, pp. 363-371, 2002.
  14. [14] D. Y. Sha and C.-Y. Hsu, “A hybrid particle swarm optimization for job shop scheduling problem,” J. of Computers & Industrial Engineering, Vol.51, No.4, pp. 791-808, 2006.
  15. [15] P. Angeline, “Evolutionary optimization versus particle swarm optimization philosophy and performance difference,” Proc. 7th Annual Conf. Evolutionary Program, pp. 601-610, 1998.
  16. [16] R. C. Eberhart and Y. Shi, “Comparison between genetic algorithms and particle swarm optimization,” Evolutionary Programming VII, New York: Springer-Verlag, Vol.1447, Lecture Notes in Computer Science, pp. 611-616, 1998.
  17. [17] F. Van den Bergh and A. P. Engelbrecht, “A new locally convergent particle swarm optimizer,” Proc. IEEE Int. Conf. Systems, Man, Cybernetics, pp. 96-101, 2002.
  18. [18] M. Lovbjerg, T. K. Rasmussen, and T. Krink, “Hybrid particle swarm optimizer with breeding and subpopulations,” Proc. Genet. Evolutionary Computing Conf., Vol.1, pp. 469-476, 2001.
  19. [19] Y. Shi and R. Eberhart, “Empirical study of particle swarm optimization,” Proc. of the Congress on Evolutionary Computation (CEC99), pp. 1945-1950, 1999.
  20. [20] W. Chen, R.-T. Zhang, Y.-M. Cai, and F.-S. Xu, “Particle swarm optimization for constrained portfolio selection problems,” Proc. of the Fifth Int. Conf. on Machine Learning and Cybernetics, pp. 2425-2429, 2006.
  21. [21] A. A. A. Esmin, G. Lambert-Torres, and A. C. Zambroni de Souza, “A hybrid particle swarm optimization applied to loss power minimization,” IEEE Trans. on Power Systems, Vol.20, No.2, pp. 859-866, 2005.
  22. [22] T. O. Ting, M. V. C. Rao, and C. K. Loo, “A novel approach for unit commitment problem via an effective hybrid particle swarm optimization,” IEEE Trans. on Power Systems, Vol.21, No.1, pp. 411-418, 2006.
  23. [23] A. P. Engelbrecht, “Computational Intelligence,” John Wiley, 2002.

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