Skip to main content
SpringerLink
Log in
Book cover

International Conference in Swarm Intelligence

ICSI 2010: Advances in Swarm Intelligence pp 558–565Cite as

A Hybrid Swarm Intelligent Method Based on Genetic Algorithm and Artificial Bee Colony

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6145))

Abstract

By integrating artificial bee colony and genetic algorithm, a novel hybrid swarm intelligent approach is proposed in this paper. The main idea of the approach is to obtain the parallel computation merit of GA and the speed and self-improvement merits of ABC by sharing information between GA population and bee colony. To exam the proposed method, it is applied to 4 benchmark functions for different dimensions. For comparison, simple GA and ABC methods are also executed. Numerical results show that the proposed hybrid swarm intelligent method is effective, and the precision could be improved.

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   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, NY (1999)

    MATH  Google Scholar 

  2. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, vol. (4), pp. 1942–1948. IEEE Service Center, Piscataway (1995)

    Chapter  Google Scholar 

  3. Dorigo, M.: Optimization, Learning and Natural Algorithms (in Italian). PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 140 (1992)

    Google Scholar 

  4. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

    Google Scholar 

  5. Basturk, B., Karaboga, D.: An artificial bee colony (abc) algorithm for numeric function optimization. In: IEEE Swarm Intelligence Symposium 2006, Indianapolis, Indiana, USA (May 2006)

    Google Scholar 

  6. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (abc) algorithm. Journal of Global Optimization 39(3), 459–471 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  7. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (abc) algorithm. Applied Soft Computing 8(1), 687–697 (2008)

    Article  Google Scholar 

  8. Karaboga, D., Akay, B.A.: Comparative Study of Artificial Bee Colony Algorithm. Applied Mathematics and Computation 214(1), 108–132 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  9. Karaboga, D., Basturk, B.: Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 789–798. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Wong, L.P., Chong, C.S.: An Efficient Bee Colony Optimization Algorithm for Traveling Salesman Problem using Frequency-based Pruning. In: Proceeding of the IEEE International Conference on Industrial Informatics, INDIN, pp. 775–782 (2009)

    Google Scholar 

  11. Chong, C.S., Low, M.Y.H., Sivakumar, A.I., Gay, K.L.: A Bee Colony Optimization Algorithm to Job Shop Schedule. In: Proceedings of the Winter Simulation Conference, pp. 1954–1961 (2006)

    Google Scholar 

  12. Fathian, M., Amiri, B., Maroosi, A.: Application of honey-bee mating optimization algorithm on clustering. Applied Mathematics and Computation 190, 1502–1513 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  13. Kang, F., Li, J.J., Xu, Q.: Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Computers and Structures 87, 861–870 (2009)

    Article  Google Scholar 

  14. Karaboga, D., Akay, B.A.: A survey: algorithms simulating bee swarm intelligence. Artif. Intell. Rev. 31, 61–85 (2009)

    Article  Google Scholar 

  15. Holland, J.H.: Adaptation in Natural and Artificial System. The University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  16. Goldberg, D.E.: Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Inner Mongolia University for Nationalities, Inner Mongolia, Tongliao, 028043

    Haiyan Zhao, Zhili Pei & Jingqing Jiang

  2. Mathematics School and Institute, Jilin University, Jilin, Changchun, 130012

    Zhili Pei

  3. College of Computer Science and Technology, Jilin University, Jilin, Changchun, 130012

    Renchu Guan & Xiaohu Shi

  4. School of applied science, Jilin Teachers‘ Institute of Engineering and Technology, Jilin, Changchun, 130052

    Chaoyong Wang

  5. State Key Lab. for Novel Software Technology, Nanjing University, Jiangsu, Nanjing, 210093

    Xiaohu Shi

Authors
  1. Haiyan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhili Pei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingqing Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Renchu Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chaoyong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaohu Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, Peking University, 100871, Beijing, China

    Ying Tan

  2. Research and Postgraduate Office, Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

  3. Department of Electrical and Computer Engineering, National University of Singapore, Engineering Drive 3, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhao, H., Pei, Z., Jiang, J., Guan, R., Wang, C., Shi, X. (2010). A Hybrid Swarm Intelligent Method Based on Genetic Algorithm and Artificial Bee Colony. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_68

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13495-1_68

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13494-4

  • Online ISBN: 978-3-642-13495-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation