Abstract
A newly developed metaheuristic swarm inspired technique named as spotted hyena optimization has got the inspiration from the hunting behavior of hyena for getting the optimal solution for complex and multidisciplinary design problems. However, in order to boost up the local and global search capability of spotted hyena optimization technique during exploration phase a chaotic search method is incorporated which works efficiently and gives positive results. The efficacy of the proposed CSHO is validated on nonlinear and constrained multidisciplinary engineering applications and 23 standard benchmark functions. It is verified and observed that proposed search algorithm, i.e., CSHO, has performed well over other existing algorithms such as spotted hyena optimization, Harris Hawk optimization, grey wolf optimizer, sine cosine algorithm, and other search algorithms.
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
Similar content being viewed by others
References
A. Asghar, S. Mirjalili, H. Faris, I. Aljarah, Harris hawks optimization: algorithm and applications. Futur. Gener. Comput. Syst. 97, 849–872 (2019)
J. Pierezan, Coyote optimization algorithm: a new metaheuristic for global optimization problems, in 2018 IEEE Congress on Evolutionary Computation (2018), pp. 1–8
G. Dhiman, V. Kumar, Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv. Eng. Softw. 114, 48–70 (2017)
R.U.I. Tang, S. Fong, R.K. Wong, K.K.L. Wong, Dynamic group optimization algorithm with embedded chaos. IEEE Access 6, 22728–22743 (2018)
D. Km, R. Sakthivel, Comparative analysis of nature inspired insect algorithms. No. Feb (2020)
S. Mirjalili, The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)
S. Arora, S. Singh, Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput. 23(3), 715–734 (2019)
S. Mirjalili, Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. (2015)
K. Fleszar, I.H. Osman, K.S. Hindi, A variable neighbourhood search algorithm for the open vehicle routing problem. Eur. J. Oper. Res. 195(3), 803–809 (2020)
V. Kumar, A. Nandi, A. Bhadoria, S. Sehgal, An intensify Harris Hawks optimizer for numerical and engineering optimization problems. Appl. Soft Comput. J. 89, 106018 (2020)
M.A. Farag, A.A. Mousa, A hybridization of sine cosine algorithm with steady state genetic algorithm for engineering design problems (vol. 2, Springer International Publishing, 2020)
R. Zhao, W.B. Haskell, An optimal algorithm for stochastic three-composite optimization. 89(1) (2019)
M. Esmaeeli, S. Golshannavaz, P. Siano, Determination of optimal reserve contribution of thermal units to afford the wind power uncertainty. J. Ambient Intell. Humaniz. Comput. (2019)
A. Tharwat, T. Gabel, T. Gabel, Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Comput. Appl. 0123456789 (2019)
M.A. Elaziz, S. Mirjalili, A hyper-heuristic for improving the initial population of whale optimization algorithm. Knowledge-Based Syst. (2019)
J. Wang, D. Wang, Particle swarm optimization with a leader and followers. Prog. Nat. Sci. 18(11), 1437–1443 (2008)
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 Singapore Pte Ltd.
About this paper
Cite this paper
Mishra, T., Singh, A.K., Kamboj, V.K. (2022). An Improved SHO Technique for Mathematical and Multidisciplinary Engineering Applications. In: Singh, P.K., Singh, Y., Kolekar, M.H., Kar, A.K., Gonçalves, P.J.S. (eds) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 832. Springer, Singapore. https://doi.org/10.1007/978-981-16-8248-3_8
Download citation
DOI: https://doi.org/10.1007/978-981-16-8248-3_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8247-6
Online ISBN: 978-981-16-8248-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)