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.
Similar content being viewed by others
References
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University Press, Erciyes
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132
Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173
Gao W, Liu S (2012) A modified artificial bee colony algorithm. Comput Oper Res 39(3):687–697
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Banharnsakun A, Achalakul T, Sirinaovakul B (2011) The best-so-far selection in artificial bee colony algorithm. Appl Soft Comput 11(2):2888–2901
Karaboga D, Akay B (2011) A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl Soft Comput 11(3):3021–3031
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):636–641
Kumar S, Kumar Sharma V, Kumari R (2014) Improved onlooker bee phase in artificial bee colony algorithm. Int J Comput Appl 90(6):20–25
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 1–6
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 1–6
Bansal JC, Sharma H, Arya K, Deep K, Pant M (2014) Self-adaptive artificial bee colony. Optimization 63(10):1513–1532
Sharma H, Bansal JC, Arya K (2013) Opposition based lévy flight artificial bee colony. Memet Comput 5(3):213–227
Sharma N, Sharma H, Sharma A (2018) Beer froth artificial bee colony algorithm for job-shop scheduling problem. Appl Soft Comput 68:507–524
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 1023–1040
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
Bansal JC, Sharma H, Jadon SS (2013) Artificial bee colony algorithm: a survey. Int J Adv Intell Paradig 5(1–2):123–159
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 145–182
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):661–678
Jadhav H, Roy R (2013) Gbest guided artificial bee colony algorithm for environmental/economic dispatch considering wind power. Expert Syst Appl 40(16):6385–6399
Bansal JC, Sharma H, Arya K, Nagar A (2013) Memetic search in artificial bee colony algorithm. Soft Comput 17(10):1911–1928
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 893–898
Sharma H, Bansal JC, Arya K, Yang X-S (2016) Lévy flight artificial bee colony algorithm. Int J Syst Sci 47(11):2652–2670
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 665–677
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 1–23
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):635–672
Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192(3):120–142
Diwold K, Aderhold A, Scheidler A, Middendorf M (2011) Performance evaluation of artificial bee colony optimization and new selection schemes. Memet Comput 1(1):1–14
Williamson D, Parker R, Kendrick J (1989) The box plot: a simple visual method to interpret data. Ann Intern Med 110(11):916
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):50–60
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sharma, S., Kumar, S. & Sharma, K. Improved Gbest artificial bee colony algorithm for the constraints optimization problems. Evol. Intel. 14, 1271–1277 (2021). https://doi.org/10.1007/s12065-019-00231-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12065-019-00231-8