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Globally Evolved Dynamic Bee Colony Optimization

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Knowledge-Based and Intelligent Information and Engineering Systems (KES 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6881))

Abstract

Bee colony optimization (BCO) is one of swarm intelligence algorithms that evolve static and locally. It performs slow improvement and tends to reach a local solution. In this paper, three modifications for the BCO are proposed, i.e. global evolution for some bees, dynamic parameters of the colony, and special treatment for the best bee. Computer simulation shows that Modified BCO performs quite better than the BCO for some job shop scheduling problems.

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References

  1. Teodorović, D., Dell’orco, M.: Bee Colony Optimization-A Cooperative Learning Approach to Complex Transportation Problems (2010)

    Google Scholar 

  2. Sivakumar, I.A., Chong, C.S., Gay, K.L., Low, M.Y.H.: A Bee Colony Optimization Algorithm to Job Shop Scheduling, pp. 1954–1961. IEEE, Los Alamitos (2006) 1- 4244-0501-7/06

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  3. Geyik, F., Cedimoglu, I.H.: The Strategies and Parameters of Tabu Search for Job Shop Scheduling. Journal of Intelligent Manufacturing 15, 439–448 (2004)

    Article  Google Scholar 

  4. Lestan, Z., Brezocnik, M., Buchmeister, B., Brezovnik, S., Balic, J.: Solving The Job-Shop Scheduling Problem With A Simple Genetic Algorithm. Int. J. Simul. Model. 8(4), 197–205 (2009)

    Article  Google Scholar 

  5. Teodorović, D.: Bee Colony Optimization BCO. In: Lim, C.P., Jain, L.C., Dehuri, S. (eds.) Innovations in Swarm Intelligence. SCI, vol. 248, pp. 39–60. Springer, Heidelberg (2009) ISBN 3642042244, 9783642042249

    Chapter  Google Scholar 

  6. Teodorović, D., Lučić, P., Marković, G., Dell’orco, M.: Bee Colony Optimization: Principles and Applications. In: 8th Seminar on Neural Network Applications in Electrical Engineering, NEUREL 2006, Belgrade, Serbia (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. The faculty of Informatics, Telkom Institute of Technology, Jl. Telekomunikasi No. 1 Terusan Buah Batu, Bandung, 40257, West Java, Indonesia

    Anggi Putri Pertiwi &  Suyanto

Authors
  1. Anggi Putri Pertiwi
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  2. Suyanto
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Editor information

Editors and Affiliations

  1. Institute of Integrated Sensor Systems, University of Kaiserslautern, Erwin-Schroedinger-str. 12, 67663, Kaiserslautern, Germany

    Andreas König

  2. Knowledge-Based Systems Group, Department of omputer Science, University of Kaiserslautern, P.O. Box 3049, 67653, Kaiserslautern, Germany

    Andreas Dengel

  3. School of Business, University of Applied Sciences Northwestern Switzerland, Riggenbachstr. 16, 4600, Olten, Switzerland

    Knut Hinkelmann

  4. Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, 599-8531, Sakai, Osaka, Japan

    Koichi Kise

  5. KES International, P.O. Box 2115, BN43 9AF, Shoreham-by-sea, UK

    Robert J. Howlett

  6. University of South Australia, Mawson Lakes, 5095, Adelaide, SA, Australia

    Lakhmi C. Jain

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© 2011 Springer-Verlag Berlin Heidelberg

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Pertiwi, A.P., Suyanto (2011). Globally Evolved Dynamic Bee Colony Optimization. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23851-2_6

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  • DOI: https://doi.org/10.1007/978-3-642-23851-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23850-5

  • Online ISBN: 978-3-642-23851-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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