Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Multi-objective Comprehensive Learning Bacterial Foraging Optimization for Portfolio Problem

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10386))

Included in the following conference series:

Abstract

Multi-objective portfolio optimization (PO) problem is always converted into a single objective problem by using the weighted method, which is sensitive to the pareto optimal front and requires that decision makers must have previous experience about the preference for weights. Based on multi-objective comprehensive learning bacterial foraging optimization (MOCLBFO), this paper proposes an algorithm which is specially designed for multi-objective PO problem. The corresponding coding strategy which considers each particle as a feasible solution is also given. In order to test the validity of the algorithm, multi-objective comprehensive learning particle swarm optimization (MOCLPSO) is chosen as the competing algorithm. Comparative experimental tests on ten assets PO problem demonstrate that MOCLBFO is able to find a more well-distributed Pareto set.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Markowitz, H.: Portfolio selection. J. Financ. 7(1), 77–91 (1952)

    Google Scholar 

  2. Shi, N.Z., Lai, M., Zheng, S.R., Zhang, B.X.: Optimal algorithms and intuitive explanations for markowitz’s portfolio selection model and sharpe’s ratio with no short-selling. Sci. Chin. Math. 51(11), 2033–2042 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  3. Niu, B., Bi, Y., Xie, T.: Structure-redesign-based bacterial foraging optimization for portfolio selection. In: Han, K., Gromiha, M., Huang, D.-S. (eds.) ICIC 2014. LNCS, vol. 8590, pp. 424–430. Springer, Heidelberg (2014)

    Google Scholar 

  4. Davis, M.H.A., Norman, A.R.: Portfolio selection with transaction costs. Math. Oper. Res. 15(4), 676–713 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen, W., Zhang, R.T., Yang, L.: Portfolio selection model based on the improved particle swarm optimization. Comput. sci. 36(1), 146–147 (2009)

    Google Scholar 

  6. Ayodele, A.A., Charles, K.A.: Improved constrained portfolio selection model using particle swarm optimization. Indian J. Sci. Technol. 8(31), 1–8 (2015)

    Google Scholar 

  7. Tan, L.J., Wang, H., Yang, C., Niu, B.: A multi-objective optimization method based on discrete bacterial algorithm for environmental/economic power dispatch. Nat. Comput., 1–7, 22 April 2017. doi:10.1007/s11047-017-9623-4

  8. Li, L., Xue, B., Tan, L.J., Niu, B.: Improved particle swarm optimizers with application on constrained portfolio selection. In: Huang, D.-S., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2010. LNCS, vol. 6215, pp. 579–586. Springer, Heidelberg (2010)

    Google Scholar 

  9. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002)

    Article  Google Scholar 

  10. Huang, V.L., Suganthan, P.N., Liang, J.J.: Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems. Int. J. Intell. Syst. 21(2), 209–226 (2006)

    Article  MATH  Google Scholar 

  11. Niu, B., Wang, H., Wang, J., Tan, L.J.: Multi-objective bacterial foraging optimization. Neurocomputing 116(116), 336–345 (2013)

    Article  Google Scholar 

Download references

Acknowledgment

This work is partially supported by The National Natural Science Foundation of China (Grants Nos. 71571120, 71271140, 61603310, 71471158, 71001072, 61472257), Natural Science Foundation of Guangdong Province (2016A030310074) and Shenzhen Science and Technology Plan (CXZZ20140418182638764), the Fundamental Research Funds for the Central Universities Nos. XDJK2014C082, XDJK2013B029, SWU114091.

Author information

Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, 518060, China

    Ben Niu, Wenjie Yi, Jia Liu & Hong Wang

  2. School of Computing, Information, and Decision Systems Engineering, Arizona State University, Tempe, USA

    Ben Niu

  3. Department of Business Management, Shenzhen Institute of Information Technology, Shenzhen, 518172, China

    Lijing Tan

  4. School of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Ya Li

  5. Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Hong Wang

Authors
  1. Ben Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjie Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lijing Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jia Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ya Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Ben Niu , Lijing Tan or Hong Wang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  4. Shenzhen University, Shenzhen, China

    Ben Niu

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Niu, B., Yi, W., Tan, L., Liu, J., Li, Y., Wang, H. (2017). Multi-objective Comprehensive Learning Bacterial Foraging Optimization for Portfolio Problem. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61833-3_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61832-6

  • Online ISBN: 978-3-319-61833-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation