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Title: A hybrid particle swarm optimization approach and its application to solving portfolio selection problems
Authors: Shamshul Bahar, Yaakob, Prof. Madya
Watada, Junzo
???metadata.dc.contributor.url???: shamshul@fuji.waseda.jp
Keywords: Genetic algorithm;Hybrid particle swarm optimization;Modern portfolio theory;Particle swarm optimization
Issue Date: Jun-2011
Publisher: Fuji Technology Press
Citation: Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 15 (4), 2011, pages 473-478
Abstract: 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 proposedmethod is a viable approach to portfolio selection.
Description: Link to publisher's homepage at http://www.fujipress.jp/
URI: http://www.fujipress.jp/finder/xslt.php?mode=present&inputfile=JACII001500040011.xml
ISSN: 1343-0130
Appears in Collections:School of Electrical Systems Engineering (Articles)

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