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Comparison of Two Swarm Intelligence Algorithms: From the Viewpoint of Learning

  • Guo-Sheng Hao
  • Ze-Hui Yi
  • Lin Wan
  • Qiu-Yi Shi
  • Ya-Li Liu
  • Gai-Ge Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

It is always said that learning is at the core of intelligence. How does learning work in swarm intelligence algorithms (SIAs)? This paper tries to answer this question by analyzing the learning mechanisms in two new emerged swarm intelligence algorithms: Krill Herd algorithm, cuckoo search. Each algorithm generates new solutions by learning to explore/exploit the promising subspace. For the new solutions generators in each algorithm, we study the learning mechanism from the viewpoint of learning scheme includes learning subject, learning object, learning result and learning rule. Also we analyze their ability of exploration and exploitation. The above study not only enables theory researchers to get the similarities and differences among SIAs, but also helps them understand the integration of different SIAs together.

Keywords

Swarm intelligence Learning mechanism Solution generators Exploitation Exploration 

Notes

Acknowledgement

Partly supported by the National Natural Science Foundation of China under Grant No. 61673196, 61503165, 61702237.

References

  1. 1.
    Smith-Miles, K., Lopes, L.: Measuring instance difficulty for combinatorial optimization problems. Comput. Oper. Res. 39(5), 875–889 (2012)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Gandomi, A.H., Alavi, A.H.: Krill Herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Gandomi, A.H., Talatahari, S., Yang, X.S., Deb, S.: Design optimization of truss structures using cuckoo search algorithm. Struct. Des. Tall Spec. Build. 22(17), 1330–1349 (2013)CrossRefGoogle Scholar
  4. 4.
    Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, pp. 210–214 (2009)Google Scholar
  5. 5.
    Hao, G.S., Shi, Q.Y., Wang, G.G., Zhang, Z.J., Zou, D.X.: Comparison of GA-based algorithms: a viewpoint of learning scheme. In: International Conference on Communications, Information Management and Network Security (CIMNS 2017), pp. 288–293 (2017)Google Scholar
  6. 6.
    Hao, G.S., Wang, G.G., Zhang, Z.J., Zou, D.X.: Comparison of PSO and ABC: from a viewpoint of learning. In: International Conference on Artificial Intelligence: Techniques and Applications (AITA 2017), pp. 108–112 (2017)Google Scholar
  7. 7.
    Hao, G.S., Gong, D.W., Shi, Y.Q., Zhang, Y., Liu, T.H.: Relation algebra based genetic algorithm model and its applications. J. Southeast Univ. (Nat. Sci. Ed.) 34(Suppl.), 58–62 (2004)Google Scholar
  8. 8.
    Gutowski, M.: Lévy flights as an underlying mechanism for global optimization algorithms. arXiv Mathematical Physics e-Prints, June 2001Google Scholar
  9. 9.
    Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Bristol (2010)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Guo-Sheng Hao
    • 1
  • Ze-Hui Yi
    • 2
  • Lin Wan
    • 1
  • Qiu-Yi Shi
    • 1
  • Ya-Li Liu
    • 1
  • Gai-Ge Wang
    • 3
  1. 1.School of Computer Science and TechnologyJiangsu Normal UniversityXuzhouChina
  2. 2.School of Computer Science and TechnologyHuaiyin Normal UniversityHuai’anChina
  3. 3.College of Information Science and EngineeringOcean University of ChinaQingdaoChina

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