Skip to main content

Parallel Particle Swarm Optimization Algorithms with Adaptive Simulated Annealing

  • Chapter
Stigmergic Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 31))

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. D. E. Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning, Addison-Wesley Publishing Company, 1989.

    Google Scholar 

  2. L. Davis, Handbook of genetic algorithms, Van Nostrand Reinhold, New York, 1991.

    Google Scholar 

  3. M. Gen, R. Cheng, Genetic algorithm and engineering design, John Wiley and Sons, New York, 1997.

    Google Scholar 

  4. M. Dorigo, V. Maniezzo, A. Colorni, The ant system: optimization by a colony of cooperating agents, IEEE Transaction on Systems, Man and Cybernetics-Part B 26 (2) (1996) 29-41.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  7. R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in: Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39-43.

    Google Scholar 

  8. J. Kennedy, R. Eberhart, Particle swarm optimization, in: IEEE International Conference on Neural Networks, 1995, pp. 1942-1948.

    Google Scholar 

  9. P. Tarasewich, P. R. McMullen, Swarm intelligence, Communications of the ACM 45 (8) (2002) 63-67.

    Article  Google Scholar 

  10. P. Angeline, Evolutionary optimization versus particle swarm optimization: philosophy and performance di?erences, in: Proc. Seventh Annual Conference on Evolutionary Programming, 1998, pp. 601-611.

    Google Scholar 

  11. R. Eberhart, Y. Shi, Comparison between genetic algorithms and particle swarm optimization, in: Proc. Seventh Annual Conference on Evolutionary Programming, 1998, pp. 611-619.

    Google Scholar 

  12. Y. Shi, R. Eberhart, Empirical study of particle swarm optimization, in: Congress on Evolutionary Computation, 1999, pp. 1945-1950.

    Google Scholar 

  13. J.-F. Chang, S. C. Chu, J. F. Roddick, J. S. Pan, A parallel particle swarm optimization algorithm with communication strategies, Journal of Information Science and Engineering 21 (4) (2005) 809-818.

    Google Scholar 

  14. S. Kirkpatrick, J. C. D. Gelatt, M. P. Vecchi, Optimization by simulated annealing, Science 220 (4598) (1983) 671-680.

    Article  MathSciNet  Google Scholar 

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

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer

About this chapter

Cite this chapter

Shu-Chuan, C., Pei-Wei, T., Jeng-Shyang, P., Jeng-Shyang, P. (2006). Parallel Particle Swarm Optimization Algorithms with Adaptive Simulated Annealing. In: Stigmergic Optimization. Studies in Computational Intelligence, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-34690-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-34690-6_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34689-0

  • Online ISBN: 978-3-540-34690-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics