Abstract
Grey wolf optimizer is a well-known optimization algorithm and is still being investigated by the researcher to improve its performance for applying in complex optimization problems. This paper introduced an adaptive grey wolf optimization algorithm (AGWO) by providing adequate exploration to the grey wolves during hunting for the prey. The exploration procedure of the original grey wolf algorithm (GWO) dominates up to half of the iterations after the remaining part of iterations dedicated to the exploitation process. This restriction in exploration leads to a lack of population diversity in the GWO. To overcome this problem, the proposed method adaptively switched to the Gaussian mutation stage with a certain probability. To assess performance, the performance of the proposed algorithm is tested with a set of 23 benchmark functions defined in the CEC2005 data suit and compared with other standard optimization algorithms along with GWO. The results reveal that the proposed algorithm exceeds the well-known optimization algorithms, the GWO.
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
Dhal KG, Ray S, Das A, Das S (2019) A survey on nature-inspired optimization algorithms and their application in image enhancement domain. Springer, Netherlands. https://doi.org/10.1007/s11831-018-9289-9
Naik MK, Panda R, Abraham A (2021) An entropy minimization based multilevel color thresholding technique for analysis of breast thermograms using equilibrium slime mould algorithm. Appl Soft Comput 107955. https://doi.org/10.1016/j.asoc.2021.107955
Zhang Y, Wang S, Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Math Probl Eng 2015:931256. https://doi.org/10.1155/2015/931256
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press
Johari NF, Zain AM, Noorfa MH, Udin A (2013) Firefly algorithm for optimization problem. In: Information technology for manufacturing systems IV, pp 512–517. Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/AMM.421.512
Horng MH (2011) Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Syst Appl 38:13785–13791. https://doi.org/10.1016/j.eswa.2011.04.180
Ventura de Oliveira P, Yamanaka K (2018) Image segmentation using multilevel thresholding and genetic algorithm: an approach. In: 2018 2nd international conference on data science and business analytics (ICDSBA), pp 380–385. https://doi.org/10.1109/ICDSBA.2018.00078
Charansiriphaisan K, Chiewchanwattana S, Sunat K (2014) A global multilevel thresholding using differential evolution approach. Math Probl Eng 2014:974024. https://doi.org/10.1155/2014/974024
Naik MK, Samantaray L, Panda R (2016) A hybrid CS–GSA algorithm for optimization BT—hybrid soft computing approaches: research and applications. Presented at the. https://doi.org/10.1007/978-81-322-2544-7_1
Bayraktar Z, Komurcu M, Bossard JA, Werner DH (2013) The wind driven optimization technique and its application in electromagnetics. IEEE Trans Antennas Propag 61:2745–2757. https://doi.org/10.1109/TAP.2013.2238654
Naik MK, Panda R, Abraham A (2021) An opposition equilibrium optimizer for context-sensitive entropy dependency based multilevel thresholding of remote sensing images. Swarm Evol Comput 100907. https://doi.org/10.1016/j.swevo.2021.100907
Guney K, Durmus A, Basbug S (2014) Backtracking search optimization algorithm for synthesis of concentric circular antenna arrays. Int J Antennas Propag 2014. https://doi.org/10.1155/2014/250841
Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algorithm for optimization: virus colony search. Adv Eng Softw 92:65–88. https://doi.org/10.1016/j.advengsoft.2015.11.004
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Wunnava A, Naik MK, Panda R, Jena B, Abraham A (2020) An adaptive Harris hawks optimization technique for two dimensional grey gradient based multilevel image thresholding. Appl Soft Comput 95:106526. https://doi.org/10.1016/j.asoc.2020.106526
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
Jena, B., Naik, M.K., Wunnava, A., Panda, R. (2022). Adaptive Grey wolf Optimization Algorithm with Gaussian Mutation . In: Mohanty, M.N., Das, S. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 430. Springer, Singapore. https://doi.org/10.1007/978-981-19-0825-5_18
Download citation
DOI: https://doi.org/10.1007/978-981-19-0825-5_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0824-8
Online ISBN: 978-981-19-0825-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)