Skip to main content

Cat Swarm Optimization

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 4099)


In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   239.00
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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Goldberg, D.E.: Genetic Algorithm in Search. In: Optimization and Machine Learning, Addison-Wesley, Reading (1989)

    Google Scholar 

  2. Pan, J.S., McInnes, F.R., Jack, M.A.: Application of Parallel Genetic Algorithm and Property of Multiple Global Optima to VQ Codevector Index Assignment. Electronics Letters 32(4), 296–297 (1996)

    CrossRef  Google Scholar 

  3. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Sixth International Symposium on Micro Machine and Human Science, pp. 39–43 (1995), 1995

    Google Scholar 

  4. Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: Congress on Evolutionary Computation, pp. 1945–1950 (1999)

    Google Scholar 

  5. Chang, J.F., Chu, S.C., Roddick, J.F., Pan, J.S.: A Parallel Particle Swarm Optimization Algorithm with Communication Strategies. Journal of Information Science and Engineering 21(4), 809–818 (2005)

    Google Scholar 

  6. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. on Evolutionary Computation 26(1), 53–66 (1997)

    CrossRef  Google Scholar 

  7. Chu, S.C., Roddick, J.F., Pan, J.S.: Ant colony system with communication strategies. Information Sciences 167, 63–76 (2004)

    CrossRef  MATH  MathSciNet  Google Scholar 

  8. Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by simulated annealing. Science, 671–680 (1983)

    Google Scholar 

  9. Huang, H.C., Pan, J.S., Lu, Z.M., Sun, S.H., Hang, H.M.: Vector quantization based on generic simulated annealing. Signal Processing 81(7), 1513–1523 (2001)

    CrossRef  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chu, SC., Tsai, Pw., Pan, JS. (2006). Cat Swarm Optimization. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36667-6

  • Online ISBN: 978-3-540-36668-3

  • eBook Packages: Computer ScienceComputer Science (R0)