Skip to main content
SpringerLink
Log in
Book cover

International Conference on Bio-Inspired Computing: Theories and Applications

BIC-TA 2016: Bio-inspired Computing – Theories and Applications pp 141–150Cite as

A Hybrid IWO Algorithm Based on Lévy Flight

  • Conference paper
  • First Online:

  • 1107 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 682))

Abstract

This paper presents a hybrid nature inspired metaheuristic algorithms, which derive from Invasive Weed Optimization (IWO) and Cuckoo Search (CS). Based on the novel and distinct qualifications of IWO and CS, we introduce a hybrid IWO algorithm and try to combine their excellent features in this extended algorithm. The efficiency of this algorithm both in the case of speed of convergence and optimality of the results are compared with IWO algorithm through a number of common multi-dimensional benchmark functions. Finally, experimental results show that the proposed approach can be successfully employed as a fast and global optimization method for a variety of theoretical or practical purposes.

This is a preview of subscription content, 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

Learn about institutional subscriptions

References

  1. Ramu, N.Y., Ojha, A.K.: Solving nonlinear constrained optimization problems by invasive weed optimization using penalty function. In: IEEE International Advance Computing Conference (IACC), Gurgaon, pp. 1326–1330 (2014)

    Google Scholar 

  2. Yang, X.-S.: Metaheuristic optimization: algorithm analysis and open problems. In: Pardalos, P.M., Rebennack, S. (eds.) SEA 2011. LNCS, vol. 6630, pp. 21–32. Springer, Heidelberg (2011). doi:10.1007/978-3-642-20662-7_2

    Chapter  Google Scholar 

  3. Holland, J.H.: Adoption in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

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

    Article  Google Scholar 

  5. Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: Corne, D., Dori-go, M., Glover, F. (eds.) New Ideas in Optimization. McGraw-Hill, England (1999)

    Google Scholar 

  6. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, pp. 4661–4667 (2007)

    Google Scholar 

  7. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-TR06, pp. 1–10 (2005)

    Google Scholar 

  8. Seyedali, M.: The ant lion optimizer. Adv. Eng. Softw. 83, 158–174 (2015)

    Google Scholar 

  9. Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1(4), 355–366 (2006)

    Article  Google Scholar 

  10. Mehrabian, A.R., Yousefi-Koma, A.: Optimal positioning of piezoelectric actuators of smart fin using bio-inspired algorithms. Aerosp. Sci. Technol. 11, 174–182 (2007)

    Article  Google Scholar 

  11. Sepehri-Rad, H., Lucas, C.: A recommender system based on invasive weed optimization algorithm. In: IEEE Congress on Evolutionary Computation, Singapore, pp. 4297–4304 (2007)

    Google Scholar 

  12. Dadalipour, B., Mallahzadeh, A.R., Davoodi-Rad, Z.: Application of the invasive weed optimization technique for antenna configurations. Prog. Electromagnet. Res. 79, 425–428 (2008). Loughborough

    Google Scholar 

  13. Sahraei-Ardakani, M., Roshanaei, M., Rahimi-Kian, A., Lucas, C.: A study of electricity market dynamics using invasive weed optimization. In: IEEE Symposium on Computational Intelligence and Games, Perth, Australia, pp. 276–282 (2008)

    Google Scholar 

  14. Xu, J.: Probe machine. IEEE Trans. Neural Netw. Learn. Syst. 27(7), 1405–1416 (2016)

    Article  MathSciNet  Google Scholar 

  15. Yang, J., Dong, C., Dong, Y.F., Liu, S., Pan, L.Q., Zhang, C.: Logic nanoparticle beacon triggered by the binding-induced effect of multiple inputs. ACS Appl. Mater. Interfaces 6(16), 14486–14492 (2014)

    Article  Google Scholar 

  16. Shi, X.L., Lu, W., Wang, Z.Y., Pan, L.Q., Cui, G.Z., Xu, J., LaBean, T.H.: Programmable DNA tile self-assembly using a hierarchical sub-tile strategy. Nanotechnology 25(7), 1–12 (2014)

    Google Scholar 

  17. Shi, X.L., Wang, Z.Y., Deng, C.Y., Song, T., Pan, L.Q., Chen, Z.H.: A novel bio-sensor based on DNA strand displacement. PLoS ONE 9(10), 1–16 (2014)

    Google Scholar 

  18. Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature Biologically Inspired Computing, India, pp. 210–214 (2009)

    Google Scholar 

  19. Dieu, N.V., Peter, S., Weerakorn, O.: Cuckoo search algorithm for non-convex economic dispatch. IET Gener. Transm. Distrib. 7(6), 645–654 (2013)

    Article  Google Scholar 

  20. Sadiq, M.S., Abubakar, B., Aiman, H.E.: Cuckoo search based resource optimization of datacenters. Appl. Intell. 44(3), 489–506 (2016)

    Article  Google Scholar 

  21. Manesh, M.H.K., Ameryan, M.: Optimal design of a solar-hybrid cogeneration cycle using Cuckoo Search algorithm. Appl. Therm. Eng. 102, 1300–1313 (2016)

    Article  Google Scholar 

  22. Tuba, M., Subotic, M., Stanarevic, N.: Modified cuckoo search algorithm for unconstrained optimization problems. In: Proceedings of the 5th European Conference on European Computing Conference, Republic of Serbia, pp. 263–268 (2011)

    Google Scholar 

  23. Walton, S., Hassan, O., Morgan, K., Brown, M.R.: Modified cuckoo search: a newgradient free optimization algorithm. Chaos, Solitons Fractals 44, 710–718 (2011)

    Article  Google Scholar 

  24. Yang, X.S., Deb, S.: Engineering optimization by cuckoo search. Int. J. Math. Model. Numer. Optim. 1(4), 330–334 (2010)

    MATH  Google Scholar 

  25. Sun, J.W., Shen, Y., Zhang, G.D., Xu, C.J., Cui, G.Z.: Combination-combination synchronization among four identical or different chaotic systems. Nonlinear Dyn. 73(3), 1211–1222 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  26. Junwei, S., Guangzhao, C., Yanfeng, W., Yi, S.: Combination complex synchronization of three chaotic complex systems. Nonlinear Dyn. 79(2), 953–965 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  27. Sun, J.W., Yin, Q., Shen, Y.: Compound synchronization for four chaotic systems of integer order and fractional order. EPL (Europhys. Lett.) 106(4), 40005–40010 (2014)

    Article  Google Scholar 

  28. Sun, J.W., Shen, Y.: Quasi-ideal memory system. IEEE Trans. Cybern. 45(7), 1353–1362 (2015)

    Article  Google Scholar 

  29. Song, T., Pan, L.: Spiking neural P systems with request rules. Neurocomputing 193(12), 193–200 (2016)

    Article  Google Scholar 

  30. Song, T., Pan, Z., Wong, D.M., Wang, X.: Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control. Inf. Sci. 372, 380–391 (2016)

    Article  Google Scholar 

  31. Wang, X., Song, T., Gong, F., Pan, Z.: On the computational power of spiking neural P systems with self-organization. Sci. Rep. doi:10.1038/srep27624

Download references

Acknowledgments

The work for this paper was supported by the National Natural Science Foundation of China (Grant Nos. 61472371, 61472372, 61572446), Basic and Frontier Technology Research Program of Henan Province (Grant Nos. 142300413214), Program for Science and Technology Innovation Talents in Universities of Henan Province (Grant No. 15HASTIT019), and Young Backbone Teachers Project of Henan province (Grant No. 2013GGJS-106).

Author information

Authors and Affiliations

  1. College of Electrical and Information Engineering, Zhengzhou University of Light Industry, No. 5 Dongfeng Road, Zhengzhou, 450002, China

    Xuncai Zhang, Xiaoxiao Wang, Guangzhao Cui & Ying Niu

Authors
  1. Xuncai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoxiao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guangzhao Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying Niu .

Editor information

Editors and Affiliations

  1. Xidian University , Xi’an, China

    Maoguo Gong

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Southwest Jiaotong University , Chengdu, China

    Gexiang Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Zhang, X., Wang, X., Cui, G., Niu, Y. (2016). A Hybrid IWO Algorithm Based on Lévy Flight. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_19

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3614-9_19

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3613-2

  • Online ISBN: 978-981-10-3614-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation