Skip to main content
SpringerLink
Log in

Galactic Gravitational Search Algorithm for Numerical Optimization

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10941))

Included in the following conference series:

Abstract

The gravitational search algorithm (GSA) has proven to be a good optimization algorithm to solve various optimization problems. However, due to the lack of exploration capability, it often traps into local optima when dealing with complex problems. Hence its convergence speed will slow down. A clustering-based learning strategy (CLS) has been applied to GSA to alleviate this situation, which is called galactic gravitational search algorithm (GGSA). The CLS firstly divides the GSA into multiple clusters, and then it applies several learning strategies in each cluster and among clusters separately. By using this method, the main weakness of GSA that easily trapping into local optima can be effectively alleviated. The experimental results confirm the superior performance of GGSA in terms of solution quality and convergence in comparison with GSA and other algorithms.

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

Access this chapter

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 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

References

  1. Cai, Y., Wang, J., Yin, J.: Learning-enhanced differential evolution for numerical optimization. Soft Comput. 16(2), 303–330 (2012)

    Article  Google Scholar 

  2. Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)

    Article  Google Scholar 

  3. Gao, S., Song, S., Cheng, J., Todo, Y., Zhou, M.: Incorporation of solvent effect into multi-objective evolutionary algorithm for improved protein structure prediction. IEEE/ACM Trans. Comput. Biol. Bioinf. (2017). https://doi.org/10.1109/TCBB.2017.2705094

  4. Gao, S., Todo, Y., Gong, T., Yang, G., Tang, Z.: Graph planarization problem optimization based on triple-valued gravitational search algorithm. IEEJ Trans. Electr. Electron. Eng. 9(1), 39–48 (2014)

    Article  Google Scholar 

  5. Gao, S., Vairappan, C., Wang, Y., Cao, Q., Tang, Z.: Gravitational search algorithm combined with chaos for unconstrained numerical optimization. Appl. Math. Comput. 231, 48–62 (2014)

    MathSciNet  Google Scholar 

  6. Gao, S., Wang, Y., Wang, J., Cheng, J.: Understanding differential evolution: a Poisson law derived from population interaction network. J. Comput. Sci. 21, 140–149 (2017)

    Article  Google Scholar 

  7. Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)

    MATH  Google Scholar 

  8. Ji, J., Gao, S., Wang, S., Tang, Y., Yu, H., Todo, Y.: Self-adaptive gravitational search algorithm with a modified chaotic local search. IEEE Access 5, 17881–17895 (2017)

    Article  Google Scholar 

  9. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)

    Article  Google Scholar 

  10. Mirjalili, S., Lewis, A.: The Whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  11. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)

    Article  Google Scholar 

  12. Song, Z., Gao, S., Yu, Y., Sun, J., Todo, Y.: Multiple chaos embedded gravitational search algorithm. IEICE Trans. Inf. Syst. 100(4), 888–900 (2017)

    Article  Google Scholar 

  13. Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report 2005005 (2005)

    Google Scholar 

  14. Wang, Y., Gao, S., Yu, Y., Xu, Z.: The discovery of population interaction with a power law distribution in brain storm optimization. Memetic Comput. (2017). https://doi.org/10.1007/s12293-017-0248-z

  15. Yang, X.S.: Nature-Inspired Optimization Algorithms. Elsevier, Amsterdam (2014)

    MATH  Google Scholar 

  16. Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)

    Article  Google Scholar 

  17. Yu, H., Xu, Z., Gao, S., Wang, Y., Todo, Y.: PMPSO: a near-optimal graph planarization algorithm using probability model based particle swarm optimization. In: IEEE International Conference on Progress in Informatics and Computing (PIC), pp. 15–19. IEEE (2015)

    Google Scholar 

  18. Yu, Y., Gao, S., Cheng, S., Wang, Y., Song, S., Yuan, F.: CBSO: a memetic brain storm optimization with chaotic local search. Memetic Comput. (2017). https://doi.org/10.1007/s12293-017-0247-0

Download references

Acknowledgment

This research was partially supported by Taizhou Science and Technology Support Social Development Project (Guidance) under No. 201701 and JSPS KAKENHI Grant Number 17K12751, 15K00332 (Japan).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Taizhou University, Taizhou, 225300, Jiangsu, China

    Sheng Li

  2. Faculty of Engineering, University of Toyama, Toyama, Japan

    Fenggang Yuan, Yang Yu, Junkai Ji & Shangce Gao

  3. Faculty of Electrical and Computer Engineering, Kanazawa University, Kanazawa-shi, 920-1192, Japan

    Yuki Todo

Authors
  1. Sheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fenggang Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junkai Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuki Todo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shangce Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shangce Gao .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

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

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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

Li, S., Yuan, F., Yu, Y., Ji, J., Todo, Y., Gao, S. (2018). Galactic Gravitational Search Algorithm for Numerical Optimization. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93815-8_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93814-1

  • Online ISBN: 978-3-319-93815-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation