Skip to main content
SpringerLink
Log in

Localized Biogeography-Based Optimization: Enhanced By Local Topologies

  • Chapter
  • First Online:
Biogeography-Based Optimization: Algorithms and Applications

  • 310 Accesses

Abstract

For heuristic algorithms that evolves a population of solutions, the population topology defines how the solutions interact with each other in the population, which often has a great influence on the performance of the algorithms. This chapter first introduces some typical types of population topologies and then describes the methods for improving BBO with local topologies.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
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. Akat SB, Gazi V (2008) Particle swarm optimization with dynamic neighborhood topology: Three neighborhood strategies and preliminary results. In: IEEE swarm intelligence symposium, pp. 152–159. IEEE. https://doi.org/10.1109/SIS.2008.4668298

  2. Chakraborty UK, Das S, Konar A (2006) Differential evolution with local neighborhood. In: Proceedings of IEEE congress on evolutionary computation, pp. 2042–2049. https://doi.org/10.1109/CEC.2006.1688558

  3. Clerc M, Kennedy J (2012) Standard PSO 2011. http://www.particleswarm.info

  4. Das S (2009) Differential evolution with a neighborhood based mutation operator: a comparative study. IEEE Trans. Evol. Comput. 13:526–553. https://doi.org/10.1109/TEVC.2008.2009457

    Article  Google Scholar 

  5. Goldberg D, Richardson J (1987) Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of 2nd international conference on genetic algorithms, pp. 41–49. https://doi.org/10.1002/cplx.20168

    Article  MathSciNet  Google Scholar 

  6. Horn J, Nafpliotis N, Goldberg DE (1994) Multiobjective optimization using the niched pareto genetic algorithm. Technical report, IlliGAL. https://doi.org/10.1109/ICEC.1994.350037

  7. Hu X, Eberhart R (2002) Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation, vol. 2, pp. 1677–1681. https://doi.org/10.1109/CEC.2002.1004494

  8. Hu Z, Cai X, Fan Z (2014) An improved memetic algorithm using ring neighborhood topology for constrained optimization. Soft Comput. 18:2023–2041. https://doi.org/10.1007/s00500-013-1183-7

    Article  Google Scholar 

  9. Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of IEEE congress on evolutionary computation. IEEE

    Google Scholar 

  10. Kennedy J, Mendes R (2006) Neighborhood topologies in fully informed and best-of-neighborhood particle swarms. IEEE Trans. Syst. Man Cybern. C. 36:515–519. https://doi.org/10.1109/TSMCC.2006.875410

    Article  Google Scholar 

  11. Li X (2010) Niching without niching parameters: Particle swarm optimization using a ring topology. IEEE Trans. Evol. Comput. 14:150–169. https://doi.org/10.1109/TEVC.2009.2026270

    Article  Google Scholar 

  12. Lin L, Wang S-x (2007) Speaker recognition based on adaptive niche hybrid genetic algorithms. Acta Electron. Sin. 35:8–12

    Google Scholar 

  13. Miller BL, Shaw MJ (1996) Genetic algorithms with dynamic niche sharing for multimodal function optimization. In: Proceedings of IEEE international conference on evolutionary computation, pp. 786–791

    Google Scholar 

  14. Mo S, Zeng J (2009) Performance analysis of the artificial physics optimization algorithm with simple neighborhood topologies. In: Proceedings of international conference on computational intelligence and security (CIS 2009), pp. 155–160. https://doi.org/10.1109/CIS.2009.195

  15. Mohais AS, Mendes R, Ward C, Posthoff C (2005) Neighborhood re-structuring in particle swarm optimization, pp. 776–785. Springer, Berlin. https://doi.org/10.1007/11589990-80

  16. Omran MGH, Engelbrecht AP, Salman A (2006) Using the ring neighborhood topology with self-adaptive differential evolution. Advances in natual computation, vol 4221. Lecture notes in computer science. Springer, Berlin, pp 976–979. https://doi.org/10.1007/11881070-129

  17. Pan QK, Suganthan PN, Liang JJ, Tasgetiren MF (2010) A local-best harmony search algorithm with dynamic subpopulations. Eng. Opt. 42:101–117. https://doi.org/10.1080/03052150903104366

    Article  Google Scholar 

  18. Qu BY, Suganthan PN, Liang JJ (2012) Differential evolution with neighborhood mutation for multimodal optimization. IEEE Trans. Evol. Comput. 16:601–614. https://doi.org/10.1109/TEVC.2011.2161873

    Article  Google Scholar 

  19. Simon D (2009) Biogeography-based optimization. http://academic.csuohio.edu/simond/bbo/

  20. Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization. Technical report, KanGAL. http://www.ntu.edu.sg/home/EPNSugan

  21. Wei L, Zhao M (2005) A niche hybrid genetic algorithm for global optimization of continuous multimodal functions. Appl. Math. Comput. 160:649–661

    MathSciNet  MATH  Google Scholar 

  22. Lin Y, Hao JM, Ji ZS, Dai YS (2000) A study of genetic algorithm based on isolation niche technique. J. Syst. Eng. 15:86–91

    Google Scholar 

  23. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3:82–102. https://doi.org/10.1109/4235.771163

    Article  Google Scholar 

  24. Zheng Y, Wu X, Ling H, Chen S (2013) A simplified biogeography-based optimization using a ring topology. Advances in swarm intelligence, vol 7928. Lecture notes in computer science. Springer, Berlin, pp 330–337

    Chapter  Google Scholar 

  25. Zheng YJ, Ling HF, Wu XB, Xue JY (2014) Localized biogeography-based optimization. Soft Comput. 18:2323–2334. https://doi.org/10.1007/s00500-013-1209-1

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hangzhou Institute of Service Engineering, Hangzhou Normal University, Hangzhou, Zhejiang, China

    Yujun Zheng

  2. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China

    Xueqin Lu, Minxia Zhang & Shengyong Chen

Authors
  1. Yujun Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xueqin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Minxia Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shengyong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yujun Zheng .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd. and Science Press, Beijing

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zheng, Y., Lu, X., Zhang, M., Chen, S. (2019). Localized Biogeography-Based Optimization: Enhanced By Local Topologies. In: Biogeography-Based Optimization: Algorithms and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-13-2586-1_3

Download citation

Publish with us

Policies and ethics

Search

Navigation