Skip to main content
SpringerLink
Log in
Book cover

Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) pp 305–318Cite as

A Bumble Bees Mating Optimization Algorithm for Global Unconstrained Optimization Problems

  • Chapter

Part of the book series: Studies in Computational Intelligence ((SCI,volume 284))

Abstract

A new nature inspired algorithm, that simulates the mating behavior of the bumble bees, the Bumble Bees Mating Optimization (BBMO) algorithm, is presented in this paper for solving global unconstrained optimization problems. The performance of the algorithm is compared with other popular metaheuristic and nature inspired methods when applied to the most classic global unconstrained optimization problems. The methods used for comparisons are Genetic Algorithms, Island Genetic Algorithms, Differential Evolution, Particle Swarm Optimization, and the Honey Bees Mating Optimization algorithm. A high performance of the proposed algorithm is achieved based on the results obtained.

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   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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. Abbass, H.A.: A monogenous MBO approach to satisfiability. In: International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2001, Las Vegas, NV, USA (2001)

    Google Scholar 

  2. Abbass, H.A.: Marriage in honey-bee optimization (MBO): a haplometrosis polygynous swarming approach. In: The Congress on Evolutionary Computation (CEC 2001), Seoul, Korea, pp. 207–214 (May 2001)

    Google Scholar 

  3. Afshar, A., Haddad, O.B., Marino, M.A., Adams, B.J.: Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. J. Franklin. Inst. 344, 452–462 (2007)

    Article  Google Scholar 

  4. Baykasoglu, A., Ozbakir, L., Tapkan, P.: Artificial bee colony algorithm and its application to generalized assignment problem. In: Chan, F.T.S., Tiwari, M.K. (eds.) Swarm Intelligence, Focus on Ant and Particle Swarm Optimization, pp. 113–144. I-Tech Education and Publishing (2007)

    Google Scholar 

  5. Clerc, M., Kennedy, J.: The particle swarm: explosion, stability and convergence in a multi-dimensional complex space. IEEE T. Evolut. Comput. 6, 58–73 (2002)

    Article  Google Scholar 

  6. Dasgupta, D. (ed.): Artificial immune systems and their application. Springer, Heidelberg (1998)

    Google Scholar 

  7. Dorigo, M., Stützle, T.: Ant colony optimization. A Bradford Book. The MIT Press, Cambridge (2004)

    Google Scholar 

  8. Drias, H., Sadeg, S., Yahi, S.: Cooperative bees swarm for solving the maximum weighted satisfiability problem. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 318–325. Springer, Heidelberg (2005)

    Google Scholar 

  9. Engelbrecht, A.P.: Computational intelligence: An introduction, 2nd edn. John Wiley and Sons, England (2007)

    Google Scholar 

  10. Fathian, M., Amiri, B., Maroosi, A.: Application of honey bee mating optimization algorithm on clustering. Appl. Math. Comput. 190, 1502–1513 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  11. Haddad, O.B., Afshar, A., Marino, M.A.: Honey-bees mating optimization (HBMO) algorithm: A new heuristic approach for water resources optimization. Water Resour. Manag. 20, 661–680 (2006)

    Article  Google Scholar 

  12. Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  13. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8, 687–697 (2008)

    Article  Google Scholar 

  14. Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE International Conference on Neural Networks 4, 1942–1948 (1995)

    Google Scholar 

  15. Marinaki, M., Marinakis, Y., Zopounidis, C.: Honey bees mating optimization algorithm for financial classification problems. Appl. Soft Comput. (2009), doi 10.1016/j.asoc.2009.09.010

    Google Scholar 

  16. Marinakis, Y., Marinaki, M.: ŞA hybrid honey bees mating optimization algorithm for the probabilistic traveling salesman problem. In: IEEE Congress on Evolutionary Computation (CEC 2009), Trondheim, Norway (2009)

    Google Scholar 

  17. Marinakis, Y., Marinaki, M., Dounias, G.: Honey bees mating optimization algorithm for the vehicle routing problem. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds.) Nature inspired cooperative strategies for optimization - NICSO 2007. Studies in Computational Intelligence, vol. 129, pp. 139–148. Springer, Berlin (2008)

    Chapter  Google Scholar 

  18. Marinakis, Y., Marinaki, M., Matsatsinis, N.: A hybrid clustering algorithm based on Honey Bees Mating Optimization and Greedy Randomized Adaptive Search Procedure. In: Maniezzo, V., Battiti, R., Watson, J.-P. (eds.) LION 2007 II. LNCS, vol. 5313, pp. 138–152. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  19. Marinakis, Y., Marinaki, M., Matsatsinis, N.: Honey bees mating optimization for the location routing problem. In: IEEE International Engineering Management Conference (IEMC – Europe 2008), Estoril, Portugal (2008)

    Google Scholar 

  20. Marinakis, Y., Marinaki, M., Dounias, G.: Honey bees mating optimization algorithm for large scale vehicle routing problems. Nat. Comput. (2009), doi 10.1007/s11047-009-9136-x

    Google Scholar 

  21. Marinakis, Y., Marinaki, M., Matsatsinis, N.: A hybrid bumble bees mating optimization – GRASP algorithm for clusterin. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 549–556. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  22. Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The bees algorithm - A novel tool for complex optimization problems. In: IPROMS 2006 Proceeding 2nd International Virtual Conference on Intelligent Production Machines and Systems, Oxford. Elsevier, Amsterdam (2006)

    Google Scholar 

  23. Teo, J., Abbass, H.A.: A true annealing approach to the marriage in honey bees optimization algorithm. Int. J. Comput. Intell. Appl. 3(2), 199–211 (2003)

    Article  Google Scholar 

  24. Teodorovic, D., Dell’Orco, M.: Bee colony optimization - A cooperative learning approach to complex transportation problems. Advanced OR and AI Methods in Transportation. In: Proceedings of the 16th Mini - EURO Conference and 10th Meeting of EWGT, pp. 51–60 (2005)

    Google Scholar 

  25. Storn, R., Price, K.: Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  26. Wedde, H.F., Farooq, M., Zhang, Y.: BeeHive: An efficient fault-tolerant routing algorithm inspired by honey bee behavior. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 83–94. Springer, Heidelberg (2004)

    Google Scholar 

  27. Yang, X.S.: Engineering optimizations via nature-inspired virtual bee algorithms. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 317–323. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  28. http://www.bumblebee.org

  29. http://www.everythingabout.net/articles/biology/animals/arthropods/insects/bees/bumble_bee

  30. http://bumbleboosters.unl.edu/biology.shtml

  31. http://www.colostate.edu/Depts/Entomology/courses/en570/papers_1998/walter.htm

Download references

Author information

Authors and Affiliations

  1. Decision Support Systems Laboratory, Department of Production Engineering and, Management, Technical University of Crete, 73100, Chania, Crete, Greece

    Yannis Marinakis & Nikolaos Matsatsinis

  2. Industrial Systems Control Laboratory, Department of Production Engineering and, Management, Technical University of Crete, 73100, Chania, Crete, Greece

    Magdalene Marinaki

Authors
  1. Yannis Marinakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Magdalene Marinaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nikolaos Matsatsinis
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and A.I. E.T.S. Ingeniería Informática y de Telecomunicación , University of Granada, C/ Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain

    Juan R. González , David Alejandro Pelta  & Carlos Cruz ,  & 

  2. School of Computer Science , University of Nottingham, Jubilee Campus Wollaton Road, NG8 1BB, Nottingham, UK

    Germán Terrazas

  3. School of Computer Science, University of Nottingham , Jubilee Campus Wollaton Road, NG8 1BB, Nottingham, UK

    Natalio Krasnogor

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Marinakis, Y., Marinaki, M., Matsatsinis, N. (2010). A Bumble Bees Mating Optimization Algorithm for Global Unconstrained Optimization Problems. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol 284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12538-6_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12538-6_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12537-9

  • Online ISBN: 978-3-642-12538-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation