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dc.contributor.authorShamshul Bahar, Yaakob, Prof. Madya-
dc.contributor.authorWatada, Junzo-
dc.date.accessioned2011-10-05T05:08:31Z-
dc.date.available2011-10-05T05:08:31Z-
dc.date.issued2011-06-
dc.identifier.citationJournal of Advanced Computational Intelligence and Intelligent Informatics, vol. 15 (4), 2011, pages 473-478en_US
dc.identifier.issn1343-0130-
dc.identifier.urihttp://www.fujipress.jp/finder/xslt.php?mode=present&inputfile=JACII001500040011.xml-
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/14027-
dc.descriptionLink to publisher's homepage at http://www.fujipress.jp/en_US
dc.description.abstractIn 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.en_US
dc.language.isoenen_US
dc.publisherFuji Technology Pressen_US
dc.subjectGenetic algorithmen_US
dc.subjectHybrid particle swarm optimizationen_US
dc.subjectModern portfolio theoryen_US
dc.subjectParticle swarm optimizationen_US
dc.titleA hybrid particle swarm optimization approach and its application to solving portfolio selection problemsen_US
dc.typeArticleen_US
dc.contributor.urlshamshul@fuji.waseda.jpen_US
Appears in Collections:School of Electrical Systems Engineering (Articles)

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