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Improved Gbest artificial bee colony algorithm for the constraints optimization problems

  • Sonal Sharma
  • Sandeep KumarEmail author
  • Kavita Sharma
Special Issue
  • 4 Downloads

Abstract

Living beings in nature are most intelligent creation of nature as they evolve with time and try to find optimum solution for each problem individually or collectively. Artificial bee colony algorithm is nature inspired algorithm that mimic the swarming behaviour of honey bee and successfully solved various optimization problems. Solution quality in artificial bee colony depends on the step size during position update. Randomly decided step size always has high possibility of miss out the exact solution. Its popular variant, namely Gbest-guided artificial bee colony algorithm tried to balance it and accomplished effectively for unconstrained optimization problems but, not satisfactory for the constrained optimization problems. Further, in the Gbest-guided artificial bee colony, individuals, which are going to update their positions, attract towards the current best solution in the swarm, which sometimes leads to premature convergence. To avoid such situation as well as to enhance the efficiency of Gbest-guided artificial bee colony to solve the unconstrained continuous optimization problems, an improved variant is proposed here. The improved Gbest-guided artificial bee colony proposed modifications in the position update during both the phase i.e. employed and onlooker bee phase to introduce diversification in search space additionally intensification of the identified region. The performance of new algorithm is evaluated for 21 benchmark optimization problems. Based on statistical analyses, it is shown that the new variant is a viable alternate of Gbest-guided artificial bee colony for the constraint optimization problems.

Keywords

Swarm intelligence Engineering optimization Nature inspired algorithm Constrained optimization 

Notes

References

  1. 1.
    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University Press, ErciyesGoogle Scholar
  2. 2.
    Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132MathSciNetzbMATHGoogle Scholar
  3. 3.
    Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173MathSciNetzbMATHGoogle Scholar
  4. 4.
    Gao W, Liu S (2012) A modified artificial bee colony algorithm. Comput Oper Res 39(3):687–697zbMATHGoogle Scholar
  5. 5.
    Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetzbMATHGoogle Scholar
  6. 6.
    Banharnsakun A, Achalakul T, Sirinaovakul B (2011) The best-so-far selection in artificial bee colony algorithm. Appl Soft Comput 11(2):2888–2901Google Scholar
  7. 7.
    Karaboga D, Akay B (2011) A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl Soft Comput 11(3):3021–3031Google Scholar
  8. 8.
    Kumar A, Kumar S, Dhayal K, Swetank D (2014) Fitness based position update in artificial bee colony algorithm. Int J Eng Res Technol 3(5):636–641Google Scholar
  9. 9.
    Kumar S, Kumar Sharma V, Kumari R (2014) Improved onlooker bee phase in artificial bee colony algorithm. Int J Comput Appl 90(6):20–25Google Scholar
  10. 10.
    Kumar S, Sharma VK, Kumari R (2014) Memetic search in artificial bee colony algorithm with fitness based position update. In: Recent advances and innovations in engineering (ICRAIE), 2014. IEEE, pp 1–6Google Scholar
  11. 11.
    Tiwari P, Kumar S (2016) Weight driven position update artificial bee colony algorithm. In: International conference on advances in computing, communication and automation (ICACCA) (Fall). IEEE, pp 1–6Google Scholar
  12. 12.
    Bansal JC, Sharma H, Arya K, Deep K, Pant M (2014) Self-adaptive artificial bee colony. Optimization 63(10):1513–1532MathSciNetzbMATHGoogle Scholar
  13. 13.
    Sharma H, Bansal JC, Arya K (2013) Opposition based lévy flight artificial bee colony. Memet Comput 5(3):213–227Google Scholar
  14. 14.
    Sharma N, Sharma H, Sharma A (2018) Beer froth artificial bee colony algorithm for job-shop scheduling problem. Appl Soft Comput 68:507–524Google Scholar
  15. 15.
    Sharma N, Sharma H, Sharma A, Bansal JC (2019) Fibonacci series-inspired local search in artificial bee colony algorithm. In: Yadav N, Yadav A, Bansal J, Deep K, Kim J (eds) Harmony search and nature inspired optimization algorithms. Springer, Berlin, pp 1023–1040Google Scholar
  16. 16.
    Sharma N, Sharma H, Sharma A, Bansal JC (2018) Grasshopper inspired artificial bee colony algorithm for numerical optimisation. J Exp Theor Artif Intell.  https://doi.org/10.1080/0952813X.2018.1552317 Google Scholar
  17. 17.
    Bansal JC, Sharma H, Jadon SS (2013) Artificial bee colony algorithm: a survey. Int J Adv Intell Paradig 5(1–2):123–159Google Scholar
  18. 18.
    Kumar S, Kumari R (2018) Artificial bee colony, firefly swarm optimization, and bat algorithms. In: Nayyar A, Le D-N, Nguyen NG (eds) Advances in swarm intelligence for optimizing problems in computer science. Chapman and Hall/CRC, Boca Raton, pp 145–182Google Scholar
  19. 19.
    Huo Y, Zhuang Y, Gu J, Ni S, Xue Y (2015) Discrete gbest-guided artificial bee colony algorithm for cloud service composition. Appl Intell 42(4):661–678Google Scholar
  20. 20.
    Jadhav H, Roy R (2013) Gbest guided artificial bee colony algorithm for environmental/economic dispatch considering wind power. Expert Syst Appl 40(16):6385–6399Google Scholar
  21. 21.
    Bansal JC, Sharma H, Arya K, Nagar A (2013) Memetic search in artificial bee colony algorithm. Soft Comput 17(10):1911–1928Google Scholar
  22. 22.
    Sharma H, Sharma S, Kumar S (2016) Lbest gbest artificial bee colony algorithm. In: 2016 International conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 893–898Google Scholar
  23. 23.
    Sharma H, Bansal JC, Arya K, Yang X-S (2016) Lévy flight artificial bee colony algorithm. Int J Syst Sci 47(11):2652–2670zbMATHGoogle Scholar
  24. 24.
    Bhambu P, Sharma S, Kumar S (2018) Modified gbest artificial bee colony algorithm. In: Pant M, Ray K, Sharma TK, Rawat S, Bandyopadhyay A (eds) Soft computing: theories and applications. Springer, Berlin, pp 665–677Google Scholar
  25. 25.
    Suganthan P, Hansen N, Liang J, Deb K, Chen Y, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. In: Proceedings of Congress on evolutionary computation (CEC), pp 1–23Google Scholar
  26. 26.
    Ali M, Khompatraporn C, Zabinsky Z (2005) A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J Glob Optim 31(4):635–672MathSciNetzbMATHGoogle Scholar
  27. 27.
    Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192(3):120–142Google Scholar
  28. 28.
    Diwold K, Aderhold A, Scheidler A, Middendorf M (2011) Performance evaluation of artificial bee colony optimization and new selection schemes. Memet Comput 1(1):1–14zbMATHGoogle Scholar
  29. 29.
    Williamson D, Parker R, Kendrick J (1989) The box plot: a simple visual method to interpret data. Ann Intern Med 110(11):916Google Scholar
  30. 30.
    Mann H, Whitney D (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18(1):50–60MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Poornima College of EngineeringJaipurIndia
  2. 2.Amity University RajasthanJaipurIndia
  3. 3.Govt. Polytechnic College KotaKotaIndia

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