Paper Info
Title
A Hybrid Particle Swarm Optimization Approach And Its Application To Solving Portfolio Selection Problems
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 trade-off 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.
Year
DOI
Venue
2011
10.20965/jaciii.2011.p0473
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS
Keywords
Field
DocType
particle swarm optimization, hybrid particle swarm optimization, modern portfolio theory, genetic algorithm
Particle swarm optimization,Mathematical optimization,Derivative-free optimization,Computer science,Meta-optimization,Modern portfolio theory,Multi-swarm optimization,Portfolio,Genetic algorithm,Metaheuristic
Journal
Volume
Issue
ISSN
15
4
1343-0130
Citations 
PageRank 
References 
0
0.34
6
Authors
2
Name
Order
Citations
PageRank
Shamshul Bahar Yaakob1265.70
Junzo Watada241184.53