Skip to main content

Honey Bee Social Foraging Algorithm for Resource Allocation

  • Chapter
Springer Handbook of Computational Intelligence

Part of the book series: Springer Handbooks ((SHB))

Abstract

Bioinspired mechanisms are an emerging area in the field of optimization, and various algorithms have been developed in the last decade. We introduce a novel bioinspired model based on the social behavior of honey bees during the foraging process, and we show how this algorithm solves a class of dynamic resource allocation problems. To illustrate the practical utility of the algorithm, we show how it can be used to solve a dynamic voltage allocation problem to achieve a maximum uniform temperature in a multizone temperature grid. Its behavior is compared with other evolutionary algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 269.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 349.99
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

Institutional subscriptions

Abbreviations

ABC:

artificial bee colony

ACO:

ant colony optimization

BCO:

bee colony optimization

DC:

direct current

GA:

genetic algorithm

PIC:

peripheral interface controller

PSO:

particle swarm optimization

PWM:

pulse width modulation

References

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

    MATH  Google Scholar 

  2. K.M. Passino: Biomimicry for Optimization, Control and Automation (Springer, London 2005)

    MATH  Google Scholar 

  3. J.H. Holland: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, 1st edn. (Univ. Michigan Press, Ann Arbor 1975)

    MATH  Google Scholar 

  4. T.P. Hong, W.Y. Lin, S.M. Liu, J.H. Lin: Dynamically adjusting migration rates for multi-population genetic algorithms, J. Adv. Comput. Intell. Intell. Inform. 11, 410–415 (2007)

    Google Scholar 

  5. M. Affenzeller, S. Winkler: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications, Vol. 6 (Chapman Hall/CRC, Boca Raton 2009)

    Book  MATH  Google Scholar 

  6. M.D. Higgins, R.J. Green, M.S. Leeson: A genetic algorithm method for optical wireless channel control, J. Ligthwave Technol. 27(6), 760–772 (2009)

    Article  Google Scholar 

  7. J. Kennedy, R.C. Eberhart: Swarm Intelligence (Morgan Kaufmann, San Francisco 2001)

    Google Scholar 

  8. M. Dorigo, C. Blum: Ant colony optimization theory: A survey, Theor. Comput. Sci. 344(2), 243–278 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  9. D. Sumpter, D.S. Broomhead: Formalising the link between worker and society in honey bee colonies. In: Multi-Agent Systems and Agent-Based Simulation, ed. by J. Sichman, R. Conte, N. Gilbert (Springer, Berlin 1998) pp. 95–110

    Chapter  Google Scholar 

  10. T.D. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, M. Zaidi: The bees algorithm – a novel tool for complex optimisation problems, Proc. 2nd Int. Virtual Conf. Intell. Prod. Mach. Syst. (Elsevier, Oxford 2006) pp. 454–461

    Google Scholar 

  11. D. Teodorovic: Bee Colony Optimization (BCO), Studies in Computational Intelligence, Vol. 248 (Springer, Berlin/Heidelberg 2009)

    Google Scholar 

  12. R.C. Eberhart, Y. Shi, J. Kennedy: Swarm Intelligence, 1st edn. (Morgan Kaufmann, San Francisco 2001)

    Google Scholar 

  13. M. Dorigo, V. Maniezzo, A. Colorni: Ant system: Optimization by a colony of cooperating agents, IEEE Trans. Syst. Man Cybern. B 26(1), 29–41 (1996)

    Article  Google Scholar 

  14. M. Dorigo, L.M. Gambardella: Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

  15. K.M. Sim, W.H. Sun: Ant colony optimization for routing and load-balancing: Survey and new directions, IEEE Trans. Syst. Man Cybern. A 33(5), 560–572 (2003)

    Article  Google Scholar 

  16. J. Zhang, H.S.-H. Chung, A.W.-L. Lo, T. Huang: Extended ant colony optimization algorithm for power electronic circuit design, IEEE Trans. Power Syst. 24(1), 147–162 (2009)

    Article  Google Scholar 

  17. D. Merkle, M. Middendorf, H. Schmeck: Ant colony optimization for resource-constrained project scheduling, IEEE Trans. Evol. Comput. 6(4), 333–346 (2002)

    Article  MATH  Google Scholar 

  18. R. Poli, J. Kennedy, T. Blackwell: Particle swarm optimization: An overview, Swarm Intell. 1(1), 33–57 (2007)

    Article  Google Scholar 

  19. A.I. Selvakumar, K. Thanushkodi: A new particle swarm optimization solution to nonconvex economic dispatch problems, IEEE Trans. Power Syst. 22(1), 42–51 (2007)

    Article  Google Scholar 

  20. Z.L. Gaing: Particle swarm optimization to solving the economic dispatch considering the generator constraints, IEEE Trans. Power Syst. 18(3), 1187–1195 (2003)

    Article  Google Scholar 

  21. Y. Liu, G. Wang, H. Chen, H. Dong, X. Zhu, S. Wang: An improved particle swarm optimization for feature selection, J. Bionic Eng. 8(2), 191–200 (2011)

    Article  Google Scholar 

  22. P.Y. Yin, J.Y. Wang: A particle swarm optimization approach to the nonlinear resource allocation problem, Appl. Math. Comput. 183(1), 232–242 (2006)

    MathSciNet  MATH  Google Scholar 

  23. S. Gheitanchi, F. Ali, E. Stipidis: Particle swarm optimization for adaptive resource allocation in communication networks, EURASIP J. Wirel. Commun. Netw. 2010, 9–21 (2010)

    Article  Google Scholar 

  24. S. Nakrani, C. Tovey: From honeybees to internet servers: Biomimicry for distributed management of internet hosting centers, Bioinspir. Biomim. 2(4), S182–S197 (2007)

    Article  Google Scholar 

  25. V. Tereshko: Reaction-diffusion model of a honeybee colony's foraging behaviour, Proc. 6th Int. Conf. Parallel Probl. Solving Nat. PPSN VI (Springer, London 2000) pp. 807–816

    Google Scholar 

  26. V. Tereshko, A. Loengarov: Collective decision making in honey-bee foraging dynamics, Comput. Inf. Syst. 9(3), 1–7 (2005)

    Google Scholar 

  27. B. Basturk, D. Karaboga: An artificial bee colony (abc) algorithm for numeric function optimization, IEEE Swarm Intell. Symp. 2006 (2006) pp. 12–14

    Google Scholar 

  28. D. Karaboga, B. Basturk: A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, J. Global Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  29. C. Ozturk, D. Karaboga, B. Gorkemli: Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm, Sensors 11(6), 6056–6065 (2011)

    Article  Google Scholar 

  30. W.Y. Szeto, Y. Wu, S.C. Ho: An artificial bee colony algorithm for the capacitated vehicle routing problem, Eur. J. Oper. Res. 215(1), 126–135 (2011)

    Article  Google Scholar 

  31. C. Zhang, D. Ouyang, J. Ning: An artificial bee colony approach for clustering, Expert Syst. Appl. 37(7), 4761–4767 (2010)

    Article  Google Scholar 

  32. D. Karaboga, B. Akay: A modified artificial bee colony (ABC) algorithm for constrained optimization problems, Appl. Soft Comput. 11(3), 3021–3031 (2011)

    Article  Google Scholar 

  33. D. Teodorović, M. Dell'Orco: Bee colony optimization – A cooperative learning approach to complex transportation problems. In: Advanced OR and AI Methods in Transportation, ed. by A. Jaszkiewicz, M. Kaczmarek, J. Zak, M. Kubiak (Publishing House of Poznan University of Technology, Poznan 2005) pp. 51–60

    Google Scholar 

  34. M. Basu: Bee colony optimization for combined heat and power economic dispatch, Expert Syst. Appl. 38(11), 13527–13531 (2011)

    Google Scholar 

  35. C.S. Chong, A.I. Sivakumar, M.Y. Low, K.L. Gay: A bee colony optimization algorithm to job shop scheduling, Proc. 38th Conf. Winter Simul. (2006) pp. 1954–1961

    Google Scholar 

  36. A. Kaur, S. Goyal: A survey on the applications of bee colony optimization techniques, Int. J. Comput. Sci. Eng. 3(8), 3037–3046 (2011)

    Google Scholar 

  37. T.D. Seeley: The Wisdom of the Hive (Harvard Univ. Press, Cambridge 1995)

    Google Scholar 

  38. K.M. Passino, T.D. Seeley: Modeling and analysis of nest-site selection by honeybee swarms: The speed and accuracy trade-off, Behav. Ecol. Sociobiol. 59(3), 427–442 (2006)

    Article  Google Scholar 

  39. G. Obando, A. Pantoja, N. Quijano: Evolutionary game theory applied to building temperature control, Proc. Nolcos (IFAC, Bologna 2010) pp. 1140–1145

    Google Scholar 

  40. A. Pantoja, N. Quijano, S. Leirens: A bioinspired approach for a multizone temperature control system, Bioinspir. Biomim. 6(1), 16007–16020 (2011)

    Article  Google Scholar 

  41. N. Quijano, K.M. Passino: The ideal free distribution: Theory and engineering application, IEEE Trans. Syst. Man Cybern. B 37(1), 154–165 (2007)

    Article  Google Scholar 

  42. N. Quijano, A.E. Gil, K.M. Passino: Experiments for dynamic resource allocation, scheduling, and control, IEEE Control Syst. Mag. 25(1), 63–79 (2005)

    Article  Google Scholar 

  43. N. Quijano, K.M. Passino: Honey bee social foraging algorithms for resource allocation: Theory and application, Eng. Appl. Artif. Intell. 23(6), 845–861 (2010)

    Article  Google Scholar 

  44. S.D. Fretwell, H.L. Lucas: On territorial behavior and other factors influencing habitat distribution in bird. I. Theoretical development, Acta Biotheor. 19, 16–36 (1970)

    Article  Google Scholar 

  45. T.D. Seeley, C.A. Tovey: Why search time to find a food-storer bee accurately indicates the relative rates of nectar collecting and nectar processing in honey bee colonies, Animal Behav. 47(2), 311–316 (1994)

    Article  Google Scholar 

  46. T.D. Seeley: Division of labor between scouts and recruits in honeybee foraging, Behav. Ecol. Sociobiol. 12, 253–259 (1983)

    Article  Google Scholar 

  47. J. Alcaraz, C. Maroto: A robust genetic algorithm for resource allocation in project scheduling, Ann. Oper. Res. 102, 83–109 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  48. S.N. Sivanandam, S.N. Deepa: Introduction to Genetic Algorithms (Springer, Berlin 2007)

    MATH  Google Scholar 

  49. M. Mitchel: An Introduction to Genetic Algorithms (MIT Press, Cambridge 1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jairo Alonso Giraldo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Giraldo, J.A., Quijano, N., Passino, K.M. (2015). Honey Bee Social Foraging Algorithm for Resource Allocation. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_70

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-43505-2_70

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43504-5

  • Online ISBN: 978-3-662-43505-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics