Abstract
Spider monkey optimization (SMO) algorithm is a recently developed optimizer that is stimulated by the extraordinary social activities of spider monkeys known as fission–fusion social structure. The SMO is developed to find solution of difficult optimization problems in real world, which are difficult to solve by the available deterministic strategies. During the solution search process in SMO, perturbation rate plays very important role. The convergence rate of SMO is highly affected by it. Usually, perturbation rate is defined by a simple function that is linearly in nature. But some application has nonlinear nature, thus a nonlinear function may improve the outcomes of SMO. For that reason, a non linear function, namely sigmoidal function used to decide perturbation in SMO and proposed strategy named as sigmoidal SMO. The investigational outcomes show the superiority of the anticipated technique over other meta-heuristics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agrawal, A., Farswan, P., Agrawal, V., Tiwari, D.C., Bansal, J.C.: On the hybridization of spider monkey optimization and genetic algorithms. In: Proceedings of Sixth International Conference on Soft Computing for Problem Solving, pp. 185–196. Springer, Berlin (2017)
Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M.: Spider monkey optimization algorithm for numerical optimization. Memetic Comput. 6(1), 31–47 (2014)
Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1, 3–18 (2011)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995. MHS’95, pp. 39–43. IEEE, New York (1995)
Gupta, K., Deep, K.: Tournament selection based probability scheme in spider monkey optimization algorithm. In: Harmony Search Algorithm, pp. 239–250. Springer, Berlin (2016)
Gupta, K., Deep, K., Bansal, J.C.: Improving the local search ability of spider monkey optimization algorithm using quadratic approximation for unconstrained optimization. Comput. Intell. 33(2), 210–240 (2017)
Gupta, K., Deep, K., Bansal, J.C.: Spider monkey optimization algorithm for constrained optimization problems. Soft Comput. 21(23), 6933–6962 (2017)
Hazrati, G., Sharma, H., Sharma, N., Bansal, J.C.: Modified spider monkey optimization. In: International Workshop on Computational Intelligence (IWCI), pp. 209–214. IEEE, New York (2016)
Kumar, S., Kumari, R., Sharma, V.K.: Fitness based position update in spider monkey optimization algorithm. Proc. Comput. Sci. 62, 442–449 (2015)
Kumar, S., Nayyar, A., Nguyen, N.G., Kumari, R.: Hyperbolic spider monkey optimization algorithm. Recent Patents Comput. Sci. 12(1) (2019)
Kumar, S., Sharma, B., Sharma, V.K., Poonia, R.C.: Automated soil prediction using bag-of-features and chaotic spider monkey optimization algorithm. Evol. Intell. pp. 1–12 (2018)
Kumar, S., Sharma, B., Sharma, V.K., Sharma, H., Bansal, J.C.: Plant leaf disease identification using exponential spider monkey optimization. Sustain. Comput.: Inf. Syst. (2018)
Kumar, S., Sharma, V.K., Kumari, R.: Modified position update in spider monkey optimization algorithm. Int. J. Emerg. Technol. Comput. Appl. Sci. 2, 198–204 (2014)
Kumar, S., Sharma, V.K., Kumari, R.: Self-adaptive spider monkey optimization algorithm for engineering optimization problems. Int. J. Inf. Commun. Comput. Technol. 2(2), 96–107 (2014)
Pal, S.S., Kumar, S., Kashyap, M., Choudhary, Y., Bhattacharya, M.: Multi-level thresholding segmentation approach based on spider monkey optimization algorithm. In: Proceedings of the Second International Conference on Computer and Communication Technologies, pp. 273–287. Springer, Berlin (2016)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)
Sharma, A., Sharma, H., Bhargava, A., Sharma, N., Bansal, J.C.: Optimal power flow analysis using lévy flight spider monkey optimisation algorithm. Int. J. Artif. Intell. Soft Comput. 5(4), 320–352 (2016)
Sharma, A., Sharma, H., Bhargava, A.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., Hazrati, G., Bansal, J.C.: Spider monkey optimization algorithm. In: Evolutionary and Swarm Intelligence Algorithms, pp. 43–59. Springer, Berlin (2019)
Simon, D.: Evolutionary Optimization Algorithms. Wiley, New York (2013)
Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. ICSI, Berkeley (1995)
Swami, V., Kumar, S., Jain, S.: An improved spider monkey optimization algorithm. In: Soft Computing: Theories and Applications, pp. 73–81. Springer, Berlin (2018)
Yang, X.-S.: Nature-Inspired Optimization Algorithms. Elsevier, Amsterdam (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, B., Sharma, V.K., Kumar, S. (2020). Sigmoidal Spider Monkey Optimization Algorithm. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_10
Download citation
DOI: https://doi.org/10.1007/978-981-15-0751-9_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0750-2
Online ISBN: 978-981-15-0751-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)