Skip to main content

Advertisement

Springer Nature Link
Log in

Animal migration optimization: an optimization algorithm inspired by animal migration behavior

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this paper, we intend to propose a new heuristic optimization method, called animal migration optimization algorithm. This algorithm is inspired by the animal migration behavior, which is a ubiquitous phenomenon that can be found in all major animal groups, such as birds, mammals, fish, reptiles, amphibians, insects, and crustaceans. In our algorithm, there are mainly two processes. In the first process, the algorithm simulates how the groups of animals move from the current position to the new position. During this process, each individual should obey three main rules. In the latter process, the algorithm simulates how some animals leave the group and some join the group during the migration. In order to verify the performance of our approach, 23 benchmark functions are employed. The proposed method has been compared with other well-known heuristic search methods. Experimental results indicate that the proposed algorithm performs better than or at least comparable with state-of-the-art approaches from literature when considering the quality of the solution obtained.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Melanie M (1999) An introduction to genetic algorithms. MIT Press, Massachusetts

    Google Scholar 

  2. Sivanandam SN, Deepa SN (2008) Introduction to genetic algorithms. Springer, Berlin

    MATH  Google Scholar 

  3. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc Int Conf Neural Netw 4:1942–1948

    Google Scholar 

  4. Engelbrecht AP (2005) Fundamentals of computational swarm intelligence. Wiley, New Jersey

    Google Scholar 

  5. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  6. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Google Scholar 

  7. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  8. Yang XS, Deb S (2009) Cuckoo search via Levy flights, in: world congress on nature & biologically inspired computing (NaBIC 2009). IEEE Publication, USA, pp 210–214

    Google Scholar 

  9. Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Modell Numer Optim 1(4):330–343

    MATH  Google Scholar 

  10. Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multiobjective optimization. Evol Comput 1:82–87

    Google Scholar 

  11. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73

    Article  Google Scholar 

  12. Mohan BC, Baskaran R (2011) Energy aware and energy efficient routing protocol for adhoc network using restructured artificial bee colony system. Commun Comput Inf Sci 169(3):473–484

    Article  Google Scholar 

  13. Rao RV, Patel VK (2011) Optimization of mechanical draft counter flow wet-cooling tower using artificial bee colony algorithm. Energy Convers Manage 52(7):2611–2622

    Article  Google Scholar 

  14. Karaboga D, Ozturk C, Karaboga N, Gorkemli B (2012) Artificial bee colony programming for symbolic regression. Inf Sci 209:1–15

    Article  Google Scholar 

  15. Li X, Yin M (2011) Hybrid differential evolution with biogeography-based optimization for design of a reconfigurable antenna array with discrete phase shifters. Int J Antennas Propag 2011. Article ID 685629

  16. Li X, Wang J, Zhou J, Yin M (2011) A perturb biogeography based optimization with mutation for global numerical optimization. Appl Math Comput 218(2):598–609

    Article  MathSciNet  MATH  Google Scholar 

  17. Li X, Yi M (2012) Multi-operator based biogeography based optimization with mutation for global numerical optimization. Comput Math Appl 64(9):2833–2844

    Article  MathSciNet  MATH  Google Scholar 

  18. Walton S, Hassan O, Morgan K, Brown MR (2011) Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Fractals 44:710–718

    Article  Google Scholar 

  19. Gandomi AH, Yang XS, Alavi AH (2011) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 27, July

  20. Layeb A (2011) A novel quantum inspired cuckoo search for knapsack problems. Int J Bio Inspir Comput 3:297–305

    Google Scholar 

  21. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82

    Article  Google Scholar 

  22. Dyer JRG, Ioanno CC, Morrell LJ, Croft DP, Couzin ID, Waters DA, Krause J (2008) Consensus decision making in human crowds. Anim Behav 75(2): 461–470

    Google Scholar 

  23. Braha D (2012) Global civil unrest: contagion, self-organization, and prediction. PLoS ONE 7(10):e48596. doi:10.1371/journal.pone.0048596

    Article  Google Scholar 

  24. Ballerini M, Cabibbo N, Candelier R, Cavagna A, Cisbani E, Giardina I, Lecomte V, Orlandi A, Parisi G, Procaccini A, Viale M, Zdravkovic V (2008) Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study. Proc Natl Acad Sci USA 105(4):1232–1237. arXiv:0709.1916. Bibcode 2008PNAS.105.1232B. doi:10.1073/pnas.0711437105. PMC 2234121. PMID 18227508.//www.ncbi.nlm.nih.gov/pmc/articles/PMC2234121/

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

    Article  MathSciNet  MATH  Google Scholar 

  26. Li X, Yin M (2013) An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure. Adv Eng Softw 55:10–31

    Article  Google Scholar 

  27. Li X, Yin M (2012) Application of differential evolution algorithm on self-potential data. PLoS ONE 7(12):e51199

    Article  Google Scholar 

  28. Rashedi Esmat, Nezamabadi-pour Hossein (2009) Saeid Saryazdi GSA: a gravitational search algorithm. Inf Sci 179:2232–2248

    Article  MATH  Google Scholar 

  29. Yang XS (2010), Firefly algorithm, L′evy flights and global optimization. In: Bramer M, Ellis R, Petridis M (eds) Research and development in intelligent systems XXVI. Springer London, pp 209–218

  30. Li X, Yin M (2012) Parameter estimation for chaotic systems by Cuckoo search algorithm using orthogonal learning method. Chin Phys B 5:50507

    Article  Google Scholar 

  31. Li X, Wang JN, Yin M (2013) Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Comput Appl. doi:10.1007/s00521-013-1354-6

    Google Scholar 

Download references

Acknowledgments

This research is fully supported by Opening Fund of Top Key Discipline of Computer Software and Theory in Zhejiang Provincial Colleges at Zhejiang Normal University under Grant No. ZSDZZZZXK37 and the Fundamental Research Funds for the Central Universities Nos. 11CXPY010.

Author information

Authors and Affiliations

  1. School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China

    Xiangtao Li, Jie Zhang & Minghao Yin

Authors
  1. Xiangtao Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jie Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Minghao Yin
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Minghao Yin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, X., Zhang, J. & Yin, M. Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput & Applic 24, 1867–1877 (2014). https://doi.org/10.1007/s00521-013-1433-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-013-1433-8

Keywords