Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Neural Networks

ISNN 2018: Advances in Neural Networks – ISNN 2018 pp 165–172Cite as

An Improved Artificial Fish Swarm Algorithm to Solve the Cutting Stock Problem

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10878))

Abstract

In order to improve the utilization rate of sheet,an improved artificial fish swarm algorithm is proposed in this paper, which improved the preying behavior and swarming behavior, meanwhile set upper and lower limit for the Congestion factor of swarming behavior. Furthermore, the proposed algorithm is used to solve the cutting stock problem. After comparing the results of the simulation experiment with the improved particle swarm algorithm in the literature and the basic artificial fish swarm algorithm, it shows that the optimal solution obtained by the improved artificial fish swarm algorithm is better than the algorithm in the literature, thus improves the utilization rate of sheet.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

References

  1. Changzheng, X.: Optimal board cutting based on simulated annealing genetic algorithm. J. LiaoNing Tech. Univ. (Nat. Sci. Ed.) 25(3), 406–408 (2006)

    Google Scholar 

  2. Xingfang, Z., Yaodong, C., Ying, Y.: A genetic algorithm for the rectangular strip packing problem. J. Comput.-Aid. Des. Comput. Graph. 20(4), 540 (2008)

    Google Scholar 

  3. Qijinbao, B., Jingqing, J., Chuyi, S.: Optimal stock cutting based on particle swarm optimization and simulated annealing. Comput. Eng. Appl. 44(26), 246–248 (2008)

    Google Scholar 

  4. Li, X., Shao, Z.: An optimizing method based on autonomous animals: fish-swarm algorithm. Syst. Eng. Theory Pract. 22(11), 2–38 (2002)

    Google Scholar 

  5. Leung, T.W., Yung, C.H., Troutt, M.D.: Applications of genetic search and simulated annealing to the two-dimensional non-guillotine cutting stock problem. Comput. Ind. Eng. 40, 201–214 (2001)

    Article  Google Scholar 

  6. Bao, L., Jiang, J., Song, C., Zhao, L., Gao, J.: Artificial fish swarm algorithm for two-dimensional non-guillotine cutting stock problem. In: Guo, C., Hou, Z.-G., Zeng, Z. (eds.) ISNN 2013. LNCS, vol. 7952, pp. 552–559. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39068-5_66

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Inner Mongolia University for Nationalities, Tongliao, 028043, China

    Chunying Cheng

  2. College of Mathematics, Inner Mongolia University for Nationalities, Tongliao, 028043, China

    Lanying Bao

Authors
  1. Chunying Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lanying Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chunying Cheng .

Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Doha, Qatar

    Tingwen Huang

  2. Sichuan University, Chengdu, China

    Jiancheng Lv

  3. Southeast University, Nanjing, China

    Changyin Sun

  4. United Institute of Informatics Problems, Minsk, Belarus

    Alexander V. Tuzikov

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cheng, C., Bao, L. (2018). An Improved Artificial Fish Swarm Algorithm to Solve the Cutting Stock Problem. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-92537-0_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92536-3

  • Online ISBN: 978-3-319-92537-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation