Abstract
In the arena of swarm intelligence algorithms, spider monkey works as a very powerful algorithm. In this article, an efficient modification of SMO is presented. In the proposed method the social learning of a spider monkey is enhanced with using the local leader of the neighboring group. The proposed algorithm is titled the social learner spider monkey optimization algorithm (SLSMO). This modified variant exploits the search space efficiently as well as the convergence speed of the algorithm is also enhanced respected the optimal solution. For validating the authenticity of this proposed SLSMO, it is collated with three benchmark sets i.e. 23 global optimization problems, 3 engineering design problems, and 16 constraint optimization problems. The attained outcomes are also collated with the significant approaches available in the literature. The obtained outcomes prove the authenticity of the propounded approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akay, B., Karaboga, D.: A modified artificial bee colony algorithm for real-parameter optimization. Inf. Sci. 192, 120–142 (2012)
Montaz Ali, M., Khompatraporn, C., Zabinsky, Z.B.: A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J. Glob. Optim. 31(4), 635–672 (2005)
Banharnsakun, A., Achalakul, T., Sirinaovakul, B.: The best-so-far selection in artificial bee colony algorithm. Appl. Soft Comput. 11(2), 2888–2901 (2011)
Bansal, J.C., Joshi, S.K., Sharma, H.: Modified global best artificial bee colony for constrained optimization problems. Comput. Electr. Eng. 67, 365–382 (2018)
Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M.: Spider monkey optimization algorithm for numerical optimization. Memetic Comput. 6(1), 31–47 (2014)
Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009)
Liang, J.J., et al.: Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization. J. Appl. Mech. 41(8), 8–31 (2006)
Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2006)
Sharma, A., Sharma, H., Bhargava, A., Sharma, N.: Power law-based local search in spider monkey optimisation for lower order system modelling. Int. J. Syst. Sci. 48(1), 150–160 (2017)
Sharma, A., Sharma, H., Bhargava, A., Sharma, N., Bansal, J.C.: Optimal placement and sizing of capacitor using limaçon inspired spider monkey optimization algorithm. Memetic Comput. 9(4), 311–331 (2017)
Sharma, H., Bansal, J.C., Arya, K.V.: Opposition based Lévy flight artificial bee colony. Memetic Comput. 5(3), 213–227 (2013)
Sharma, H., Bansal, J.C., Arya, K.V., Yang, X.-S.: Lévy flight artificial bee colony algorithm. Int. J. Syst. Sci. 47(11), 2652–2670 (2016)
Sharma, N., Sharma, H., Sharma, A.: Beer froth artificial bee colony algorithm for job-shop scheduling problem. Appl. Soft Comput. 68, 507–524 (2018)
Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317–325 (2003)
Yang, X.-S., Karamanoglu, M.: Swarm intelligence and bio-inspired computation: an overview. In: Swarm Intelligence and Bio-Inspired Computation, pp. 3–23. Elsevier (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sharma, A., Sharma, N., Sharma, K. (2022). Enhancing the Social Learning Ability of Spider Monkey Optimization Algorithm. In: Sharma, H., Vyas, V.K., Pandey, R.K., Prasad, M. (eds) Proceedings of the International Conference on Intelligent Vision and Computing (ICIVC 2021). ICIVC 2021. Proceedings in Adaptation, Learning and Optimization, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-97196-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-97196-0_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97195-3
Online ISBN: 978-3-030-97196-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)