LGWO: An Improved Grey Wolf Optimization for Function Optimization

  • Jie Luo
  • Huiling ChenEmail author
  • Kejie Wang
  • Changfei Tong
  • Jun Li
  • Zhennao Cai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)


Grey wolf optimization (GWO) algorithm is a novel nature-inspired heuristic paradigm. GWO was inspired by grey wolves, which mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. It has exhibited promising performance in many fields. However, GWO algorithm has the drawback of slow convergence and low precision. In order to overcome this drawback, we propose an improved version of GWO enhanced by the Lévy-flight strategy, termed as LGWO. Lévy-flight strategy was introduced into the GWO to find better solutions when the grey wolves fall into the local optimums. The effectiveness of LGWO has been rigorously evaluated against ten benchmark functions. The experimental results demonstrate that the proposed approach outperforms the other three counterparts.


Grey wolf optimization Function optimization Lévy-flight 



This research is supported by the National Natural Science Foundation of China (NSFC) (61303113, 61402337). This research is also funded by the Zhejiang Provincial Natural Science Foundation of China (LY17F020012, LQ13G010007, LQ13F020011 and LY14F020035), the Science and Technology Plan Project of Wenzhou, China (G20140048, H20110003).


  1. 1.
    Holland, J.H.: Genetic algorithms. Sci. Am. 267, 66–72 (1992)CrossRefGoogle Scholar
  2. 2.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization artificial ants as a computational intelligence technique. IEEE Comput. Intell. Mag. 1, 28–39 (2006)CrossRefGoogle Scholar
  3. 3.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks. IEEE, vol. 4, pp. 1942–1948. IEEE Press, Piscataway (1995)Google Scholar
  4. 4.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  5. 5.
    Sulaiman, M.H., Mustaffa, Z., Mohamed, M.R., Aliman, O.: Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Appl. Soft Comput. J. 32, 286–292 (2015)CrossRefGoogle Scholar
  6. 6.
    Li, Q., Chen, H., Huang, H., Zhao, X., Cai, Z., Tong, C., Liu, W., Tian, X.: An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Comput. Math. Methods Med. 2017 (2017)Google Scholar
  7. 7.
    Kamaruzaman, A.F., Zain, A.M., Yusuf, S.M., Udin, A.: Lévy flight algorithm for optimization problems—a literature review. Appl. Mech. Mater. 421, 496–501 (2013)CrossRefGoogle Scholar
  8. 8.
    Jensi, R., Jiji, G.W.: An enhanced particle swarm optimization with levy flight for global optimization. Appl. Soft Comput. 43, 248–261 (2016)CrossRefGoogle Scholar
  9. 9.
    Tang, D., Yang, J., Dong, S., Liu, Z.: A lévy flight-based shuffled frog-leaping algorithm and its applications for continuous optimization problems. Appl. Soft Comput. 49, 641–662 (2016)CrossRefGoogle Scholar
  10. 10.
    Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27, 1053–1073 (2016)CrossRefGoogle Scholar
  11. 11.
    Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04944-6_14 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jie Luo
    • 1
  • Huiling Chen
    • 1
    Email author
  • Kejie Wang
    • 1
  • Changfei Tong
    • 1
  • Jun Li
    • 1
  • Zhennao Cai
    • 1
  1. 1.College of Physics and Electronic InformationWenzhou UniversityWenzhouChina

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