Skip to main content
SpringerLink
Log in

Swarm Intelligence and Evolutionary Computation: Overview and Analysis

  • Chapter
  • First Online:
Recent Advances in Swarm Intelligence and Evolutionary Computation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 585))

Abstract

In many applications, the complexity and nonlinearity of the problems require novel and alternative approaches to problem solving. In recent years, nature-inspired algorithms, especially those based on swarm intelligence, have become popular, due to the simplicity and flexibility of such algorithms. Here, we review briefly some recent algorithms and then outline the self-tuning framework for parameter tuning. We also discuss some convergence properties of the cuckoo search and the bat algorithm. Finally, we present some open problems as further research topics.

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

References

  1. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948. Piscataway, NJ (1995)

    Google Scholar 

  2. Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, UK (2008)

    Google Scholar 

  3. Yang, X.S.: Cuckoo Search and Firefly Algorithm: Theory and Applications, Studies in Computational Intelligence, vol. 516, Springer, Berlin (2014)

    Google Scholar 

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

    Google Scholar 

  5. Ashby, W.R.: Princinples of the self-organizing sysem. In: Von Foerster, H., Zopf, G.W., Jr. (eds.) Pricinples of Self-Organization: Transactions of the University of Illinois Symposium, pp. 255–278. Pergamon Press, London (1962)

    Google Scholar 

  6. Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrite optimization. Artif. Life 5(2), 137–172 (1999)

    Article  Google Scholar 

  7. Fister, I., Fister Jr, I., Yang, X.S., Brest, J.: A comprehensive review of firefly algorithms. Swarm Evol. Comput. 13(1), 34–46 (2013)

    Article  Google Scholar 

  8. Fister, I., Yang, X.S., Brest, J., Fister Jr, I.: Modified firefly algorithm using quaternion representation. Expert Syst. Appl. 40(18), 7220–7230 (2013)

    Article  Google Scholar 

  9. Fister, I., Mernik, M., Filipic, B.: Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm. Comput. Optim. Appl. 54(3), 741–770 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  10. Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: Proceedings of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE Publications, USA (2009)

    Google Scholar 

  11. Pavlyukevich, I.: Lévy flights, non-local search and simulated annealing J. Comput. Phys. 226(2), 1830–1844 (2007)

    Google Scholar 

  12. Yang, X.S., Deb, S.: Engineering optimization by cuckoo search. Int. J. Math. Model. Num. Optim. 1(4), 330–343 (2010)

    MATH  Google Scholar 

  13. Yang, X.S., Deb, S.: Multiobjective cuckoo search for design optimization. Comput. Oper. Res. 40(6), 1616–1624 (2013)

    Article  MathSciNet  Google Scholar 

  14. Yang, X.S., Deb, S.: Cuckoo search: recent advances and applications. Neural Comput. Appl. 24(1), 169–174 (2014)

    Article  Google Scholar 

  15. Yang, X.S., Deb, S., Fong, S.: Accelerated particle swarm optimization and support vector machine for business optimization and applications. In: Networked Digital Technologies, Communications in Computer and Information Science, vol. 136, pp. 53–66 (2011)

    Google Scholar 

  16. Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimisation (NICSO 2010), Studies in Computational Intelligence, vol. 284, pp. 65–74. Springer, New York (2010)

    Google Scholar 

  17. Yang, X.S.: Bat algorithm for multi-objective optimisation. Int. J. Bio-Inspired Comput. 3(5), 267–274 (2011)

    Google Scholar 

  18. Fister Jr, I., Fister, D., Yang, X.S.: A hybrid bat algorithm. Elektrotehniski Vestn. 80(1–2), 1–7 (2013)

    Google Scholar 

  19. Yang, X.S., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 1–18 (2012)

    Article  MATH  Google Scholar 

  20. Yang, X.S., He, X.S.: Bat algorithm: literature review and applications. Int. J. Bio-inspired Comput. 5(3), 141–149 (2013)

    Article  Google Scholar 

  21. Yang, X.S.: Flower pollination algorithm for global optimization. In: Unconventional Computation and Natural Computation, pp. 240–249. Springer, New York (2012)

    Google Scholar 

  22. Yang, X.S.: Flower pollination algorithm for global optimization. In: Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, vol. 7445, pp. 240–249. Springer, New York (2012)

    Google Scholar 

  23. Yang, X.S., Karamanoglu, M., He, X.S.: Multi-objective flower algorithm for optimization. Procedia Comput. Sci. 18(1), 861–868 (2013)

    Article  Google Scholar 

  24. Yang, X.S., Karamanoglu, M., He, X.S.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46(9), 1222–1237 (2014)

    Article  MathSciNet  Google Scholar 

  25. Storn, R.: On the usage of differential evolution for function optimization. Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 519–523. Berkeley, CA (1996)

    Google Scholar 

  26. Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  27. Price, K., Storn, R., Lampinen, J.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Berlin (2005)

    Google Scholar 

  28. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization: Harmony search. Simulation 76(2), 60–68 (2001)

    Article  Google Scholar 

  29. Booker, L., Forrest, S., Mitchell, M., Riolo, R.: Perspectives on Adaptation in Natural and Artificial Systems. Oxford University Press, Oxford (2005)

    Google Scholar 

  30. Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Anbor (1975)

    Google Scholar 

  31. Yang, X.S., Deb, S., Loomes, M., Karamanoglu, M.: A framework for self-tuning optimization algorithm. Neural Comput. Appl. 23(7–8), 2051–2057 (2013)

    Article  Google Scholar 

  32. Eiben, A.E., Smit, S.K.: Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol. Comput. 1(1), 19–31 (2011)

    Article  Google Scholar 

  33. Belavkin, R.V.: Optimal measures and Markov transition kernels. J. Global Optim. 55(2), 387–416 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  34. Wang, F., He, X.S., Wang, Y., Yang, S.M.: Markov model and convergence analysis based on cuckoo search algorithm. Comput. Eng. 38(11), 180–185 (2012). (in Chinese)

    Google Scholar 

  35. Huang, G.Q., Zhao, W.J., Lu, Q.Q.: Bat algorithm with global convergence for solving large-scale optimization problem. Appl. Res. Comput. 30(5), 1323–1328 (2013). (in Chinese)

    Google Scholar 

  36. Ren, Z.H., Wang, J., Gao, Y.L.: The global convergence of particle swarm optimization based on Markov chain. Control Theory Appl. 2011, 462–466 (2011). (in Chinese)

    Google Scholar 

  37. Blum, C., Roli, A.: Metaheuristics in combinatorial optimisation: overview and conceptural comparision. ACM Comput. Surv. 35(2), 268–308 (2003)

    Article  Google Scholar 

  38. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Science and Technology, Middlesex University, London, NW4 4BT, UK

    Xin-She Yang

  2. College of Science, Xi’an Polytechnic University, No. 19 Jinhua South Road, Xi’an, People’s Republic of China

    Xingshi He

Authors
  1. Xin-She Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xingshi He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin-She Yang .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Yang, XS., He, X. (2015). Swarm Intelligence and Evolutionary Computation: Overview and Analysis. In: Yang, XS. (eds) Recent Advances in Swarm Intelligence and Evolutionary Computation. Studies in Computational Intelligence, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-319-13826-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13826-8_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13825-1

  • Online ISBN: 978-3-319-13826-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation