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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1106

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contributor.authorMishra, S K-
contributor.authorPanda, G-
contributor.authorMeher, S-
date.accessioned2009-12-16T02:56:18Z-
date.available2009-12-16T02:56:18Z-
date.issued2009-
identifier.citationWorld Congress on Nature and Biologically Inspired Computiing -NaBIC 2009, 9-11 Dec 2009, Coimbatore,Indiaen
identifier.urihttp://hdl.handle.net/2080/1106-
descriptionCopyright belongs to proceedings publishersen
description.abstractThe problem of portfolio optimization is a standard problem in financial world and has received a lot of attention. Selecting an optimal weighting of assets is a critical issue for which the decision maker takes several aspects into consideration. In this paper we consider a multi-objective problem in which the percentage of each available asset is selected such a way that the total profit of the portfolio is maximized while total risk to be minimized, simultaneously. Four well-known multiobjective evolutionary algorithms i.e. Parallel Single Front Genetic Algorithm (PSFGA), Strength Pareto Evolutionary Algorithm 2(SPEA2), Nondominated Sorting Genetic Algorithm II( NSGA II) and Multi Objective Particle Swarm Optimization (MOPSO) for solving the bi-objective portfolio optimization problem has been applied. Performance comparison carried out in this paper by performing different numerical experiments. These experiments are performed using real-world data. The results show that MOPSO outperforms other two for the considered test cases.en
format.extent387031 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherPSG College of Technologyen
subjectMultiobjective optimizationen
subjectPareto optimal solutionsen
subjectglobal optimizationen
subjectcrowding distanceen
subjectportfolio optimizationen
titleMulti-objective Particle swarm optimization Approach to Portfolio Optimizationen
typeArticleen
Appears in Collections:Conference Papers

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