Skip to main content
SpringerLink
Log in

Research Hotspots and Trends in Swarm Intelligence: From 2000 to 2015

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9712))

Included in the following conference series:

  • 1754 Accesses

Abstract

Swarm Intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. This paper was to explore a bibliometric approach to quantitatively assessing current research hotspots and trends on Swarm Intelligence, using the related literature in the Science Citation Index (SCI) database from 2000 to 2015. Articles referring to Swarm Intelligence were concentrated on the analysis of scientific outputs, distribution of countries, institutions, periodicals, subject categories and research performances by individuals. Moreover, innovative methods such as keyword co-citation analysis, semantic clustering and Keyword Frequent Burst Detection were applied to provide a dynamic view of the evolution of swarm intelligence research hotpots and trends from various perspectives which may serve as a potential guide for future research.

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. Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. In: Proceeding NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, 26–30 June 1989

    Google Scholar 

  2. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence - From Natural to Artificial Systems. Oxford University Press Inc, New York (1999)

    MATH  Google Scholar 

  3. Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)

    Article  Google Scholar 

  4. Karaboga, D., Akay, B.: A survey: algorithms simulating bee swarm intelligence. Artif. Intell. Rev. 31(1–4), 61–85 (2009)

    Article  Google Scholar 

  5. Martens, D., Baesens, B., Fawcett, T.: Editorial survey: swarm intelligence for data mining. Mach. Learn. 82(1), 1–42 (2011)

    Article  MathSciNet  Google Scholar 

  6. Huang, E.H., Socher, R., Manning, C.D., Ng, A.Y.: Improving word representations via global context and multiple word prototypes. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol. 1, pp. 873–882. Association for Computational Linguistics (2012)

    Google Scholar 

  7. Rogosa, D., Brandt, D., Zimowski, M.: A growth curve approach to the measurement of change. Psychol. Bull. 92(3), 726 (1982)

    Article  Google Scholar 

  8. Chen, C.: CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inform. Sci. Technol. 57(3), 359–377 (2006)

    Article  Google Scholar 

  9. Freeman, L.C.: Centrality in social networks conceptual clarification. Social Networks 1(3), 215–239 (1979)

    Article  Google Scholar 

  10. Li, X., Yao, X.: Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans. Evol. Comput. 16(2), 210–224 (2012)

    Article  MathSciNet  Google Scholar 

  11. Kannan, S., Slochanal, S.M.R., Subbaraj, P., Padhy, N.P.: Application of particle swarm optimization technique and its variants to generation expansion planning problem. Electr. Power Syst. Res. 70(3), 203–210 (2004)

    Article  Google Scholar 

  12. Yang, X., Yuan, J., Yuan, J., Mao, H.: A modified particle swarm optimizer with dynamic adaptation. Appl. Math. Comput. 189(2), 1205–1213 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  13. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) Algorithm. J. Global Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  14. Mernik, M., Liu, S.H., Karaboga, D., Črepinšek, M.: On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation. Inf. Sci. 291, 115–127 (2015)

    Article  MathSciNet  Google Scholar 

  15. Zou, F., Wang, L., Hei, X., Chen, D., Yang, D.: Teaching–learning-based optimization with dynamic group strategy for global optimization. Inf. Sci. 273, 112–131 (2014)

    Article  Google Scholar 

  16. Nanda, S.J., Panda, G.: A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol. Comput. 16, 1–18 (2014)

    Article  Google Scholar 

  17. Kaveh, A., Sheikholeslami, R., Talatahari, S., Keshvari-Ilkhichi, M.: Chaotic swarming of particles: a new method for size optimization of truss structures. Adv. Eng. Softw. 67, 136–147 (2014)

    Article  Google Scholar 

  18. Hassanien, A.E., Moftah, H.M., Azar, A.T., Shoman, M.: MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier. Appl. Soft Comput. 14, 62–71 (2014)

    Article  Google Scholar 

  19. Huang, C.L., Huang, W.C., Chang, H.Y., Yeh, Y.C., Tsai, C.Y.: Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering. Appl. Soft Comput. 13(9), 3864–3872 (2013)

    Article  Google Scholar 

  20. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv preprint arXiv:1301.3781

Download references

Author information

Authors and Affiliations

  1. Center for National Security and Strategic Studies (CNSSS), National University of Defense Technology, Changsha, 410073, China

    Zili Li, Li Zeng, Hua Zhong & Jinhong Wu

Authors
  1. Zili Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hua Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinhong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Zeng .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Li, Z., Zeng, L., Zhong, H., Wu, J. (2016). Research Hotspots and Trends in Swarm Intelligence: From 2000 to 2015. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-41000-5_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40999-3

  • Online ISBN: 978-3-319-41000-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation