Skip to main content
SpringerLink
Log in

An improved QPSO algorithm and its application in fuzzy portfolio model with constraints

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Aiming at the shortcomings of quantum-behaved particle swarm optimization algorithm (QPSO), an improved quantum-behaved particle swarm optimization algorithm (IQPSO) is put forward, and the improved algorithm is applied in solving a kind of fuzzy portfolio selection problems. Firstly, a kind of portfolio models with fuzzy return rates and background risk is established followed by some necessary preparations of fuzzy theory. Then, in the improved algorithm, hybrid probability distribution strategy and contraction–expansion coefficient with nonlinear structure are chosen to enhance particle’s exploration ability, and premature prevention mechanism is used to maintain population diversity. Furthermore, the experimental results on 16 benchmark functions show that IQPSO has better convergence and robustness than PSO with inertia weight, QPSO and QPSO with a hybrid probability distribution in most cases. Finally, when solving a fuzzy portfolio model, IQPSO provides comparable and superior results compared with the other metaheuristics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Baptista AM (2008) Optimal delegated portfolio management with background risk. J Bank Finance 32(6):977–985

    Article  Google Scholar 

  • Cai X, Teo KJ, Yang XQ et al (2004) Minimax portfolio optimization: empirical numerical study. J Oper Res Soc 55:65–72

    Article  Google Scholar 

  • Carlsson C, Fuller R (2001) On possibilistic mean value and variance of fuzzy numbers. Fuzzy Set Syst 122(2):315–326

    Article  MathSciNet  Google Scholar 

  • Clerc M, Kennedy J (2002) The particle swarm-explosion, stability and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  • Hamza F, Janssen J (1998) The mean-semivariances approach to realistic portfolio optimization subject to transaction costs. Appl Stoch Models Data Anal 14(4):275–283

    Article  MathSciNet  Google Scholar 

  • He G, Huang NJ (2012) A modified particle swarm optimization algorithm with applications. Appl Math Comput 219(3):1053–1060

    MathSciNet  MATH  Google Scholar 

  • He G, Huang NJ (2014) A new particle swarm optimization algorithm with an application. Appl Math Comput 232(3):521–528

    MathSciNet  MATH  Google Scholar 

  • Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks, pp 1942–1948

  • Li LL, Jiao LC, Zhao JQ et al (2017) Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering. Pattern Recogn 63:1–14

    Article  Google Scholar 

  • Li YY, Bai XY, Jiao LC et al (2017) Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Appl Soft Comput 56:345–356

    Article  Google Scholar 

  • Liu TY, Jiao LC, Ma WP et al (2016) Quantum-behaved particle swarm optimization with collaborative attractors for nonlinear numerical problems. Commun Nonlinear Sci Numer Simul 44:167–183

    Article  Google Scholar 

  • Luo HX, Jia PF, Qiao SQ et al (2017) Enhancing electronic nose performance based on a novel QPSO RBM technique. Sens Actuators B-Chem 259:241–249

    Article  Google Scholar 

  • Markowitz H (1952) Portfolio selection. J Finance 7(1):77–91

    Google Scholar 

  • Meng XB, Gao XZ, Lu LH et al (2016) A new bio-inspired optimisation algorithm: bird swarm algorithm. J Exp Theor Artif Intell 28(4):1–15

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69(3):46–61

    Article  Google Scholar 

  • Mohadeseh S, Hossein N, Malihe M (2012) A quantum behaved gravitational search algorithm. Intell Inf Manag 4(6):390–395

    Google Scholar 

  • Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26(2):69–74

    Article  Google Scholar 

  • Parsopoulos K, Vrahatis MN (2002) Particle swarm optimization method for constrained optimization problems. Eur Int Symp Comput Intell 76:214–220

    MATH  Google Scholar 

  • Shi YH, Obaiahnahatti BG (1998) A modified particle swarm optimizer. IEEE Conf Evol Comput 6:69–73

    Google Scholar 

  • Sun J, Feng B, Xu WB (2004) Particle swarm optimization with particles having quantum behavior. IEEE Cong Evol Comput 1:325–331

    Google Scholar 

  • Sun J, Xu WB, Feng B (2004) A global search strategy of quantum-behaved particle swarm optimization. IEEE Cong Cybern Intell Syst 1:111–116

    Google Scholar 

  • Sun J, Xu WB, Liu J (2005) Parameter selection quantum behaved particle swarm optimization. Int Conf Nat Comput 3612:543–552

    Google Scholar 

  • Sun J, Xu WB, Fang W (2006) Quantum-behaved particle swarm optimization with a hybrid probability distribution. Lect Notes Artif Intell 4099:737–746

    Google Scholar 

  • Sun J, Fang W, Wu XJ et al (2012) Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection. Evol Comput 20(3):349–393

    Article  Google Scholar 

  • Tsanakas A (2008) Risk measurement in the presence of background risk. Insur Math Econ 42(2):520–528

    Article  MathSciNet  Google Scholar 

  • Wang YJ, Shi XM (2019) Jumping dolphin swarm algorithm. Control Theory Appl 36(10):1755–1767

    MATH  Google Scholar 

  • Yang XS, Deb S (2009) Cuckoo search via L\(\acute{e}\)vy flights. In: World congress nature biologically inspired computing, pp 210–214

  • Yang XS (2010) A new metaheuristic bat-inspired algorithm. Nat Inspired Coop Strat Optim 284:65–74

    MATH  Google Scholar 

  • Yang ZL, Wu A (2020) A non-revisiting quantum-behaved particle swarm optimization based multilevel thresholding for image segmentation. Neural Comput Appl 32:12011–12031

    Article  Google Scholar 

  • Zhang F (2020) Intelligent task allocation method based on improved QPSO in multi-agent system. J Ambient Intel Hum Comput 11:655–662

    Article  Google Scholar 

  • Zhang WG, Wang YL, Chen ZP et al (2007) Possibilistic mean-variance models and efficient frontiers for portfolio selection problem. Inform Sci 177(13):2787–2801

    Article  MathSciNet  Google Scholar 

  • Zhang WG, Xiao WL, Xu WJ (2010) A possibilistic portfolio adjusting model with new added assets. Econ Model 27(1):208–213

    Article  Google Scholar 

Download references

Funding

The work is funded by Chongqing Municipal Science and Technology Project (No.cstc2020jcyj-msxm3740, No.cstc2020jcyj-msxm3651), Chongqing Municipal Education Commission Project (No.KJ1500631), Doctoral Research Project of Chongqing Technology and Business University (No.2015-56-08) and Youth Project of Chongqing Technology and Business University (No.1552004).

Author information

Authors and Affiliations

  1. College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, 400067, People’s Republic of China

    Guang He

  2. Research Center for Economy of Upper Reaches of the Yangtze River, Chongqing Technology and Business University, Chongqing, 400067, People’s Republic of China

    Xiao-li Lu

Authors
  1. Guang He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao-li Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guang He.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest to this work.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by Vladik Kreinovich.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, G., Lu, Xl. An improved QPSO algorithm and its application in fuzzy portfolio model with constraints. Soft Comput 25, 7695–7706 (2021). https://doi.org/10.1007/s00500-021-05688-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-05688-3

Keywords

Search

Navigation