Advertisement

The Research of Improved Wolf Pack Algorithm Based on Differential Evolution

  • Yingxiang WangEmail author
  • Minyou Chen
  • Tingli Cheng
  • Muhammad Arshad Shehzad Hassan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 924)

Abstract

Aiming at the problems of traditional wolf pack algorithm (WPA): easy to fall into local optimal, large computational resource cost and low robustness, an improved wolf pack algorithm based on differential evolution (DIWPA) is proposed. By introducing the search factor for search wolves, maximum number of raid wolves, adaptive siege step size and differential evolution strategy, the proposed algorithm can not only reduce the computational cost but also improve the global search ability. The DIWPA is used to conduct optimization test on 12 benchmark functions and compare to 3 typical optimization algorithms. The test results show that DIWPA has great robustness and global search ability, especially has excellent performance in multi-peak, high-dimension, indivisible functions.

Keywords

Wolf pack algorithm Local optimal Differential evolution Robustness Global search ability 

References

  1. 1.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: 1995 Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (2002)Google Scholar
  2. 2.
    Tehsin, S., Rehman, S., Saeed, M.O.B., et al.: Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition. IEEE Access PP(99), 1 (2017)Google Scholar
  3. 3.
    Wei, L.X., Li, X., Fan, R., et al.: A hybrid multi-objective particle swarm optimization algorithm based on R2 indicator. IEEE Access PP(99), 1 (2018)Google Scholar
  4. 4.
    Singhal, P.K., Naresh, R., Sharma, V.: Binary fish swarm algorithm for profit-based unit commitment problem in competitive electricity market with ramp rate constraints. Gener. Transm. Distrib. IET 9(13), 1697–1707 (2015)CrossRefGoogle Scholar
  5. 5.
    Li, X.L., Shao, Z.J., Qian, J.X.: An optimizing method based on autonomous animate: fish swarm algorithm. Syst. Eng.-Theory Pract. 22, 32–38 (2002)Google Scholar
  6. 6.
    Li, Y., Zhang, C., Yang, Q., et al.: Improved ant colony algorithm for adaptive frequency-tracking control in WPT system. IET Microwaves Antennas Propag. 12, 23–28 (2017)Google Scholar
  7. 7.
    Wu, L.H.: Differential Evolution Algorithm and Its Application. Hunan University (2007)Google Scholar
  8. 8.
    Yang, C., Tu, X., Chen, J.: Algorithm of marriage in honey bees optimization based on the wolf pack search. In: International Conference on Intelligent Pervasive Computing, pp. 462–467. IEEE Computer Society (2007)Google Scholar
  9. 9.
    Wu, H.S., Zhang, F.G., Wu, L.H.: A new swarm intelligence algorithm—wolf pack algorithm. Syst. Eng. Electron. 35(11), 2430–2438 (2013)zbMATHGoogle Scholar
  10. 10.
    Zhang, L., Zhang, L., Liu, S., et al.: Three-dimensional underwater path planning based on modified wolf pack algorithm. IEEE Access 5(99), 22783–22795 (2017)CrossRefGoogle Scholar
  11. 11.
    Wang, J.Q., Jia, Y.Y., Xiao, Q.Y.: Wolf pack algorithm in the optimal operation of hydropower station reservoirs. Adv. Sci. Technol. Water Resour. 35(3), 1–4 (2015)Google Scholar
  12. 12.
    Hui, X.B., Guo, Q., Wu, P.P., et al.: An improved wolf pack algorithm. Control Decis. 32(7), 1163–1172 (2017)zbMATHGoogle Scholar
  13. 13.
    Qian, R.X.: A wolf algorithm based on cultural mechanism. Inf. Technol. 2015(12), 98–102 (2015)Google Scholar
  14. 14.
    Cao, S., Jiancheng, A.: Otsu image segmentation method for improved wolf group optimization algorithm. Microelectron. Comput. 34(10), 16–21 (2017)Google Scholar
  15. 15.
    Solano-Aragón, C., Castillo, O.: Optimization of benchmark mathematical functions using the firefly algorithm with dynamic parameters. In: Castillo, O., Melin, P. (eds.) Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics. SCI, vol. 574, pp. 81–89. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-10960-2_5CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Yingxiang Wang
    • 1
    Email author
  • Minyou Chen
    • 1
  • Tingli Cheng
    • 1
  • Muhammad Arshad Shehzad Hassan
    • 1
  1. 1.State Key Laboratory of Power Transmission Equipment and System Security and New Technology, School of Electrical EngineeringChongqing UniversityChongqingChina

Personalised recommendations