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A new particle swarm optimization algorithm with an application

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  • He, Guang
  • Huang, Nan-jing

Abstract

In this paper, for dealing with the portfolio model from stocks market, a new particle swarm optimization algorithm (NPSO) is presented, in which the optimal and sub-optimal positions of each particle are considered in the iteration process, and the crossover operation is used to avoid premature. It is demonstrated from optimization tests that NPSO outperforms existed PSO. Then NPSO is used to solve a discontinuous programming model, and four different optimal portfolio selections are displayed which are denoted by S1,S2,S3 and S4, respectively. Finally, actual return rates of these portfolios are obtained, and it is analyzed from related graphs that S2 and S3 gain better results.

Suggested Citation

  • He, Guang & Huang, Nan-jing, 2014. "A new particle swarm optimization algorithm with an application," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 521-528.
    • Handle: RePEc:eee:apmaco:v:232:y:2014:i:c:p:521-528
    DOI: 10.1016/j.amc.2014.01.028
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    References listed on IDEAS

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    1. Xiaoqiang Cai & Kok-Lay Teo & Xiaoqi Yang & Xun Yu Zhou, 2000. "Portfolio Optimization Under a Minimax Rule," Management Science, INFORMS, vol. 46(7), pages 957-972, July.
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    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. K.L. Teo & X.Q. Yang, 2001. "Portfolio Selection Problem with Minimax Type Risk Function," Annals of Operations Research, Springer, vol. 101(1), pages 333-349, January.
    5. Martin R. Young, 1998. "A Minimax Portfolio Selection Rule with Linear Programming Solution," Management Science, INFORMS, vol. 44(5), pages 673-683, May.
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    Cited by:

    1. Doering, Jana & Kizys, Renatas & Juan, Angel A. & Fitó, Àngels & Polat, Onur, 2019. "Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends," Operations Research Perspectives, Elsevier, vol. 6(C).

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