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:
Shamshul Bahar Yaakob and Junzo 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.
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