Abstract
The artificial bee colony algorithm (ABCA) has established itself as a signature algorithm in the area of swarm intelligence based algorithms. The hybridization of the local search technique enhances the exploitation capability in the search process of the global optimization strategies. In this article, an effective local search technique that is designed by taking inspiration by Limaçon curve, is incorporated in ABCA and the designed strategy is named Limaçon inspired ABC (LABC) algorithm. The exploitation capability of the LABC strategy is tested over 18 complex benchmark optimization problems. The test results are compared with similar state-of-art algorithms and statistical analysis shows the LABC can be considered an effective variant of the ABC algorithms to solve the complex optimization problems.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192:120–142
Banharnsakun A, Achalakul T, Sirinaovakul B (2011) The best-so-far selection in artificial bee colony algorithm. Appl Soft Comput 11(2):2888–2901
Bansal JC, Sharma H, Arya K, Deep K, Pant M (2014) Self-adaptive artificial bee colony. Optimization 63(10):1513–1532
Bansal JC, Sharma H, Arya K, Nagar A (2013) Memetic search in artificial bee colony algorithm. Soft Comput 17(10):1911–1928
Bansal JC, Sharma H, Jadon SS (2013) Artificial bee colony algorithm: a survey. Int J Adv Intell Paradig 5(1):123–159
Jadon SS, Bansal JC, Tiwari R, Sharma H (2014) Expedited artificial bee colony algorithm. In: Proceedings of the third international conference on soft computing for problem solving, pp 787–800
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697
Sharma A, Sharma H, Bhargava A, Sharma N (n.d.) Fibonacci series based local search in spider monkey optimisation for transmission expansion planning. Int J Swarm Intell (in press)
Sharma A, Sharma H, Bhargava A, Sharma N, Bansal JC (2017) Optimal placement and sizing of capacitor using Limaçon inspired spider monkey optimization algorithm. Memet Comput 9(4):311–331
Sharma H, Bansal JC, Arya K (2013) Opposition based Lévy flight artificial bee colony. Memet Comput 5(3):213–227
Sharma H, Bansal JC, Arya K (2014) Power law-based local search in artificial bee colony. Int J Artif Intell Soft Comput 4(2/3):164–194
Sharma H, Bansal JC, Arya K, Yang X-S (2016) Lévy flight artificial bee colony algorithm. Int J Syst Sci 47(11):2652–2670
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 (2015) Black hole artificial bee colony algorithm. In: International conference on swarm, evolutionary, and memetic computing, pp 214–221
Vermeij GJ (1995) A natural history of shells. Princeton University Press, Princeton
Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173
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, K., Gupta, P.C. & Sharma, N. Limaçon inspired artificial bee colony algorithm for numerical optimization. Evol. Intel. 14, 1345–1353 (2021). https://doi.org/10.1007/s12065-020-00430-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12065-020-00430-8