Skip to main content
SpringerLink
Log in

Self-adaptive Percolation Behavior Water Cycle Algorithm

  • Conference paper
  • First Online:
Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

Included in the following conference series:

Abstract

Water cycle algorithm is a new meta-heuristic optimization algorithm based on the observation of water cycle and how rivers and streams flow downhill towards the sea in the real world. In this paper, a new self-adaptive water cycle algorithm with percolation behavior is proposed. The percolation behavior is introduced to accelerate the convergence speed of proposed algorithm. At the same time, a self-adaptive rainfall process can generate the new stream, more and more new position can be explored, consequently, increasing the diversity of population. Eight typical benchmark functions are tested, the simulation results show that the proposed algorithm is feasible and effective than basic water cycle algorithm, and demonstrate that this proposed algorithm has superior approximation capabilities in high-dimensional space.

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

Similar content being viewed by others

References

  1. Cocke, T., Moscicki, Z., Agarwal, R.: Optimization of hydrofoils using a genetic algorithm. J. Aircraft 51, 78–89 (2014)

    Article  Google Scholar 

  2. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks 4, pp. 1942–1948 (1995)

    Google Scholar 

  3. Kirkpatrick Jr., S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 650–671 (1983)

    Article  MathSciNet  Google Scholar 

  4. Yongquan, Z., Jiakun, L., Guangwei, Z.: Leader glowworm swarm optimization algorithm for solving nonlinear equations systems. Przeglad Elektrotechniczny 88, 101–106 (2012)

    Google Scholar 

  5. Alatas, B.: Chaotic harmony search algorithms. Appl. Math. Comput. 216, 2687–2699 (2010)

    Article  Google Scholar 

  6. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22, 52–68 (2002)

    Article  Google Scholar 

  7. Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1, 355–366 (2006)

    Article  Google Scholar 

  8. Yang, X.-S.: A new metaheuristic Bat-inspired algorithm. Studies Comput. Intell. 284, 65–74 (2010)

    Article  Google Scholar 

  9. Oftadeh, R., Mahjoob, M.J., Shariatpanahi, M.: A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput. Math. Appl. 60(7), 2087–2099 (2010)

    Article  MATH  Google Scholar 

  10. Eskandar, H., Sadollah, A., Bahreininejad, A., et al.: Water cycle algorithm-a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput. Struct. 1, 151–166 (2012)

    Article  MATH  Google Scholar 

  11. Eskandar, H., Sadollah, A., Bahreininejad, A.: Weight optimization of truss structures using water cycle algorithm. Int. J. Optim. Civil Eng. 3, 115–129 (2013)

    Google Scholar 

  12. Sadollah, A., Eskandar, H.: Water cycle algorithm for solving multi-objective optimization problems. Appl. Soft Comput. 27, 279–298 (2014)

    Article  Google Scholar 

  13. Chun, Z., Wei, L.G., Chun, L.L.: Optimizations of space truss structures using WCA algorithm, vol. 16, pp. 35−38 (2014)

    Google Scholar 

  14. A. Hedar, function http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedarfiles/TestGofiles/

  15. Kitahara, T., Mizuno, S.: A bound for the number of different basic solutions generated by the simplex method 137, 579–586 (2013)

    MathSciNet  MATH  Google Scholar 

  16. Kheirfam, B., Verdegay, J.L.: The dual simplex method and sensitivity analysis for fuzzy linear programming with symmetric trapezoidal numbers. Fuzzy Optim. Decis. Making 12, 171–189 (2013)

    Article  MathSciNet  Google Scholar 

  17. Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15, 4–31 (2011)

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  19. Xing, Y., Wang, Y.: Assembly sequence planning based on a hybrid particle swarm optimization and genetic algorithm. Int. J. Prod. Res. 50, 7303–7315 (2012)

    Article  MATH  Google Scholar 

  20. Wang. L., Gong. Y.: A fast shuffled frog leaping algorithm. In: 2013 Ninth International Conference on Natural Computation (ICNC). IEEE, pp. 369–373 (2013)

    Google Scholar 

  21. Karaboga, D., Bastuk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459–477 (2007)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work is supported by National Science Foundation of China under Grants No.61165015; 61463007.

Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, 530006, China

    Shilei Qiao, Yongquan Zhou, Rui Wang & Yuxiang Zhou

  2. Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Nanning, 530006, China

    Yongquan Zhou

Authors
  1. Shilei Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongquan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuxiang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongquan Zhou .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Qiao, S., Zhou, Y., Wang, R., Zhou, Y. (2015). Self-adaptive Percolation Behavior Water Cycle Algorithm. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22180-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation