Abstract
Salp swarm algorithm (SSA) is a recently developed meta-heuristic swarm intelligence optimization algorithm based on simulating the chain movement behavior of salps sailing and foraging in the sea. In this paper, a novel hybrid meta-heuristic algorithm called SSA-FGWO is proposed to overcome the shortcomings of the original SSA, including slow convergence speed in dealing with high-dimensional and multimodal landscapes, low precision, and global optimization problems. The essential idea of SSA-FGWO is to improve the salp swarm optimization algorithm (SSA) by utilizing the Grey Wolf Algorithm (GWO) strategy. The hybridization mechanism includes two steps: First, the strong exploitation of SSA is applied to update the leaders' position of the chain population. Second, the strong exploration strategy of GWO is used to update the followers' position to increase the population variance of the proposed optimizer. The proposed algorithm is expected to enhance exploration and exploitation ability significantly by the hybrid design. The effectiveness of SSA-FGWO is investigated through CEC2020, 23 representative benchmark cases, and feature selection problems (18 data sets) are solved. The algorithm has been examined for its computational complexity and convergent behavior. In addition to GWO, SSA and other swarm optimization algorithms are employed to compare with the proposed optimizer. The obtained experimental results show that the exploitation, exploration tendencies, and convergence patterns of SSA-FGWO have significantly improved. The results show that the proposed SSA-FGWO algorithm is a promising one that outperforms the basic SSA, GWO, and other algorithms in terms of efficacy.
Similar content being viewed by others
References
Physics RF-F of, 1986 undefined Quantum mechanical computers. mathweb.zju.edu.cn
Feynman RP (1982) Simulating physics with computers. Int J Theor Phys 21:467–488. https://doi.org/10.1007/BF02650179
Dhiman G, Kumar V (2019) Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowledge-Based Syst 165:169–196. https://doi.org/10.1016/j.knosys.2018.11.024
Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377. https://doi.org/10.1016/j.eswa.2020.113377
Salgotra R, Singh U (2019) The naked mole-rat algorithm. Neural Comput Appl 31:8837–8857. https://doi.org/10.1007/s00521-019-04464-7
Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70. https://doi.org/10.1016/j.advengsoft.2017.05.014
Mohamed AAA, Hassan SA, Hemeida AM et al (2020) Parasitism – predation algorithm (PPA): a novel approach for feature selection. Ain Shams Eng J 11:293–308. https://doi.org/10.1016/j.asej.2019.10.004
Zhao W, Zhang Z, Wang L (2020) Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 87:103300. https://doi.org/10.1016/j.engappai.2019.103300
Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowledge-Based Syst 191:105190. https://doi.org/10.1016/j.knosys.2019.105190
Eberhart R, Kennedy J (1995) New optimizer using particle swarm theory. In: Proceedings of the international symposium on micro machine and human science. IEEE, pp 39–43
Zawbaa HM, Emary E, Grosan C (2016) Feature selection via chaotic antlion optimization. PLoS ONE 11:e0150652. https://doi.org/10.1371/JOURNAL.PONE.0150652
Mirjalili S, Gandomi AH, Mirjalili SZ et al (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
Emary E, Zawbaa HM, Grosan C, Hassenian AE (2015) Feature subset selection approach by gray-wolf optimization. Adv Intell Syst Comput 334:1–13. https://doi.org/10.1007/978-3-319-13572-4_1
Komaki GM, Kayvanfar V (2015) Grey wolf optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time. J Comput Sci 8:109–120. https://doi.org/10.1016/j.jocs.2015.03.011
El-Fergany AA, Hasanien HM (2015) Single and multi-objective optimal power flow using grey wolf optimizer and differential evolution algorithms. Electr Power Components Syst 43:1548–1559. https://doi.org/10.1080/15325008.2015.1041625
Kamboj VK, Bath SK, Dhillon JS (2016) Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer. Neural Comput Appl 27:1301–1316. https://doi.org/10.1007/s00521-015-1934-8
Fan Q, Chen Z, Zhang W, Fang X (2020) ESSAWOA: enhanced whale optimization algorithm integrated with salp swarm algorithm for global optimization. Eng Comput. https://doi.org/10.1007/s00366-020-01189-3
Zhang J, Wang JS (2020) Improved salp swarm algorithm based on levy flight and sine cosine operator. IEEE Access 8:99740–99771. https://doi.org/10.1109/ACCESS.2020.2997783
Sayed GI, Khoriba G, Haggag MH (2018) A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell 48:3462–3481. https://doi.org/10.1007/s10489-018-1158-6
Abbassi R, Abbassi A, Heidari AA, Mirjalili S (2019) An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers Manag 179:362–372. https://doi.org/10.1016/j.enconman.2018.10.069
Faris H, Mafarja MM, Heidari AA et al (2018) An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems. Knowledge-Based Syst 154:43–67. https://doi.org/10.1016/j.knosys.2018.05.009
El-Fergany AA (2018) Extracting optimal parameters of PEM fuel cells using Salp Swarm Optimizer. Renew Energy 119:641–648. https://doi.org/10.1016/j.renene.2017.12.051
Zhang Q, Chen H, Heidari A, et al Chaos-induced and mutation-driven schemes boosting salp chains-inspired optimizers. ieeexplore.ieee.org
Ahmed S, Mafarja M, Faris H, Aljarah I (2018) Feature selection using salp swarm algorithm with chaos. In: ACM International Conference Proceeding Series. Association for Computing Machinery, New York, USA, pp 65–69
Yang B, Zhong L, Zhang X et al (2019) Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition. J Clean Prod 215:1203–1222. https://doi.org/10.1016/j.jclepro.2019.01.150
Ibrahim RA, Ewees AA, Oliva D et al (2019) Improved salp swarm algorithm based on particle swarm optimization for feature selection. J Ambient Intell Humaniz Comput 10:3155–3169. https://doi.org/10.1007/s12652-018-1031-9
Long W, Wu T, Liang X et al (2018) Solving high-dimensional global optimization problems using an improved sine cosine algorithm. Elsevier 123:108–126. https://doi.org/10.1016/j.eswa.2018.11.032
Sepesy Maučec M, Brest J (2019) A review of the recent use of Differential Evolution for Large-Scale Global Optimization: An analysis of selected algorithms on the CEC 2013 LSGO benchmark suite. Swarm Evol Comput 50:100428. https://doi.org/10.1016/J.SWEVO.2018.08.005
Yildiz Y, Sciences AT-I, (2019) undefined Large scale continuous global optimization based on micro differential evolution with local directional search. Elsevier
Baş E, Ülker E (2021) Improved social spider algorithm for large scale optimization. Artif Intell Rev 54:3539–3574. https://doi.org/10.1007/S10462-020-09931-5
Neggaz N, Ewees AA, Elaziz MA, Mafarja M (2020) Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection. Expert Syst Appl 145:113103. https://doi.org/10.1016/j.eswa.2019.113103
Tubishat M, Idris N, Shuib L et al (2020) Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection. Expert Syst Appl 145:113122. https://doi.org/10.1016/j.eswa.2019.113122
Panda N, Majhi SK (2020) Improved Salp Swarm Algorithm with Space Transformation Search for Training Neural Network. Arab J Sci Eng 45:2743–2761. https://doi.org/10.1007/s13369-019-04132-x
Elaziz MA, Li L, Jayasena KPN, Xiong S (2020) Multiobjective big data optimization based on a hybrid salp swarm algorithm and differential evolution. Appl Math Model 80:929–943. https://doi.org/10.1016/j.apm.2019.10.069
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
Turky AM, Abdullah S (2014) A multi-population harmony search algorithm with external archive for dynamic optimization problems. Inf Sci (Ny) 272:84–95. https://doi.org/10.1016/J.INS.2014.02.084
Frank A, Asuncion A (2010) {UCI} Machine Learning Repository
Abdel-Basset M, El-Shahat D, El-henawy I et al (2020) A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection. Expert Syst Appl 139:112824. https://doi.org/10.1016/j.eswa.2019.112824
Mirjalili S, Lewis A (2013) S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization. Swarm Evol Comput 9:1–14. https://doi.org/10.1016/j.swevo.2012.09.002
Qais MH, Hasanien HM, Alghuwainem S (2019) Enhanced salp swarm algorithm: Application to variable speed wind generators. Eng Appl Artif Intell 80:82–96. https://doi.org/10.1016/j.engappai.2019.01.011
Faris H, Heidari AA, Al-Zoubi AM et al (2020) Time-varying hierarchical chains of salps with random weight networks for feature selection. Expert Syst Appl 140:112898. https://doi.org/10.1016/j.eswa.2019.112898
Gandomi AH, Yang XS (2011) Benchmark problems in structural optimization. Stud Comput Intell 356:259–281. https://doi.org/10.1007/978-3-642-20859-1_12
Gupta S, Deep K, Moayedi H et al (2020) Sine cosine grey wolf optimizer to solve engineering design problems. Eng Comput 1:3. https://doi.org/10.1007/s00366-020-00996-y
Industry CC-C in, 2000 undefined Use of a self-adaptive penalty approach for engineering optimization problems. Elsevier
Singh N, Son LH, Chiclana F, Magnot JP (2020) A new fusion of salp swarm with sine cosine for optimization of non-linear functions. Eng Comput 36:185–212. https://doi.org/10.1007/s00366-018-00696-8
Pathak VK, Srivastava AK (2020) A novel upgraded bat algorithm based on cuckoo search and Sugeno inertia weight for large scale and constrained engineering design optimization problems. Eng Comput 1:3. https://doi.org/10.1007/s00366-020-01127-3
Belkourchia Y, Azrar L, Zeriab ESM (2019) A hybrid optimization algorithm for solving constrained engineering design problems. In: 2019 International conference on optimization and applications, ICOA 2019. Institute of electrical and electronics engineers inc.
Li S, Chen H, Wang M et al (2020) Slime mould algorithm: A new method for stochastic optimization. Futur Gener Comput Syst 111:300–323. https://doi.org/10.1016/j.future.2020.03.055
He Q, Wang L (2007) A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 186:1407–1422. https://doi.org/10.1016/j.amc.2006.07.134
He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20:89–99. https://doi.org/10.1016/j.engappai.2006.03.003
Mezura-Montes E, Coello CAC, Velázquez-Reyes J, Muñoz-Dávila L (2007) Multiple trial vectors in differential evolution for engineering design. In: Engineering Optimization. Taylor & Francis , pp 567–589
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
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
Qaraad, M., Amjad, S., Hussein, N.K. et al. Large scale salp-based grey wolf optimization for feature selection and global optimization. Neural Comput & Applic 34, 8989–9014 (2022). https://doi.org/10.1007/s00521-022-06921-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-022-06921-2