Advertisement

Fitness-Based Controlled Movements in Artificial Bee Colony Algorithm

  • Harish SharmaEmail author
  • Kritika Sharma
  • Nirmala Sharma
  • Assif Assad
  • Jagdish Chand Bansal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1048)

Abstract

Artificial Bee Colony (ABC) is an efficient metaheuristic algorithm is used for solving various complex optimization problems. A new variant of ABC, namely, fitness-based controlled movements in ABC (ConABC) is presented here. In ConABC, an Intelligent Term (IT) is introduced in the employed bee stage, which enhances the solution search ability of the ABC algorithm. The IT is actually controlling the step size of an individual according to its fitness. The presented algorithm is extensively inferred to 12 benchmark functions. It is then compared with ABC, its two recent variants, titled Best-So-Far ABC (BSFABC), Modified ABC (MABC) and some more state-of-the-art algorithms. The observational outcomes unfold that ConABC has potential to solve the problems in a better way than ABC algorithm.

Keywords

Nature inspired algorithms Collective behaviour Guided search Artificial bee colony 

References

  1. 1.
    Akay, B., Karaboga, D.: A modified artificial bee colony algorithm for real-parameter optimization. Inf. Sci. 192, 120–142 (2012)CrossRefGoogle Scholar
  2. 2.
    Banharnsakun, A., Achalakul, T., Sirinaovakul, B.: The best-so-far selection in artificial bee colony algorithm. Appl. Soft Comput. 11(2), 2888–2901 (2011)CrossRefGoogle Scholar
  3. 3.
    Bansal, J.C., Sharma, H., Arya, K.V., Deep, K., Pant, M.: Self-adaptive artificial bee colony. Optimization 63(10), 1513–1532 (2014)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M.: Spider monkey optimization algorithm for numerical optimization. Memet. Comput. 6(1), 31–47 (2014)CrossRefGoogle Scholar
  5. 5.
    Bansal, J.C., Sharma, H., Nagar, A., Arya, K.V.: Balanced artificial bee colony algorithm. Int. J. Artif. Intell. Soft Comput. 3(3), 222–243 (2013)CrossRefGoogle Scholar
  6. 6.
    Das, S., Biswas, A., Dasgupta, S., Abraham, A.: Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. In: Foundations of Computational Intelligence, vol. 3, pp. 23–55. Springer (2009)Google Scholar
  7. 7.
    Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)CrossRefGoogle Scholar
  8. 8.
    Eusuff, M., Lansey, K., Pasha, F.: Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng. Optim. 38(2), 129–154 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Fister Jr, I., Yang, X.-S., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization (2013). arXiv:1307.4186
  10. 10.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)Google Scholar
  11. 11.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)CrossRefGoogle Scholar
  12. 12.
    Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2011)Google Scholar
  13. 13.
    Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Stat. Comput. 4(2), 87–112 (1994)Google Scholar
  14. 14.
    Luthra, I., Chaturvedi, S.K., Upadhyay, D., Gupta, R.: Comparative study on nature inspired algorithms for optimization problem. In: 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA), vol. 2, pp. 143–147. IEEE (2017)Google Scholar
  15. 15.
    Marrow, P.: Nature-inspired computing technology and applications. BT Technol. J. 18(4), 13–23 (2000)CrossRefGoogle Scholar
  16. 16.
    Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: Gsa: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)CrossRefGoogle Scholar
  17. 17.
    Sharma, H., Sharma, S., Kumar. S.: Lbest gbest artificial bee colony algorithm. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 893–898. IEEE (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Harish Sharma
    • 1
    Email author
  • Kritika Sharma
    • 1
  • Nirmala Sharma
    • 1
  • Assif Assad
    • 2
  • Jagdish Chand Bansal
    • 3
  1. 1.Rajasthan Technical University (R.T.U.)KotaIndia
  2. 2.IUSTAwantiporaIndia
  3. 3.South Asian UniversityNew DelhiIndia

Personalised recommendations