Abstract
Grey Wolf Optimizer (GWO) is a robust algorithm used for solving optimization problems. GWO is a population-oriented algorithm and simulates the social governorship and hunting nature of grey wolves. Data envelopment analysis (DEA) is a data-oriented decision support technique that helps decision-makers select the best potential solutions among a group of candidate solutions. In the current study, we propose GWO algorithm for solving DEA problems. Moreover, we present a real-life application of the proposed approach to the education sector.
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References
Kumar P, Mogha SK, Pant M (2012) Differential evolution for data envelopment analysis. In: Proceedings of the international conference on soft computing for problem solving (SocProS 2011) December 20–22, 2011. Springer, pp. 311–319
Cooper WW, Seiford L, Tone K (2000) Data envelopment analysis: theory, methodology, and applications, references and dea-solver software
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Soft 69:46–61
Emary E, Yamany W, Hassanien AE, Snasel V (2015) Multi-objective gray-wolf optimization for attribute reduction. Proc Comput Sci 65:623–632
Emary E, Zawbaa HM, Grosan C, Hassenian AE (2015) Feature subset selection approach by gray-wolf optimization. In: Afro-European conference for industrial advancement. Springer, pp 1–13
Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381
Medjahed SA, Saadi TA, Benyettou A, Ouali M (2016) Gray wolf optimizer for hyperspectral band selection. Appl Soft Comput 40:178–186
Mirjalili S (2015) How effective is the grey wolf optimizer in training multi-layer perceptrons. Appl Intell 43(1):150–161
Mohamed AAA, El-Gaafary AA, Mohamed YS, Hemeida AM (2015) Design static var compensator controller using artificial neural network optimized by modify grey wolf optimization. In: 2015 International joint conference on neural networks (IJCNN). IEEE, pp 1–7
Mosavi M, Khishe M, Ghamgosar A (2016) Classification of sonar data set using neural network trained by gray wolf optimization. Neural Netw World 26(4):393
Elhariri E, El-Bendary N, Hassanien AE (2016) A hybrid classification model for emg signals using grey wolf optimizer and svms. In: The 1st international conference on advanced intelligent system and informatics (AISI2015), November 28–30, 2015. Springer, Beni Suef, Egypt, pp 297–307
Elhariri E, El-Bendary N, Hassanien AE, Abraham A (2015) Grey wolf optimization for one-against-one multi-class support vector machines. In: 2015 7th International conference of soft computing and pattern recognition (SoCPaR). IEEE, pp 7–12
Kumar V, Chhabra JK, Kumar D (2017) Grey wolf algorithm-based clustering technique. J Intell Syst 26(1):153–168
Das KR, Das D, Das J (2015) Optimal tuning of pid controller using gwo algorithm for speed control in dc motor. In: 2015 International conference on soft computing techniques and implementations (ICSCTI). IEEE, pp 108–112
Li SX, Wang JS (2015) Dynamic modeling of steam condenser and design of pi controller based on grey wolf optimizer. Math Prob Eng
Wong LI, Sulaiman M, Mohamed M, Hong MS (2014) Grey wolf optimizer for solving economic dispatch problems. In: 2014 IEEE international conference on power and energy (PECon). IEEE, pp 150–154
Jayabarathi T, Raghunathan T, Adarsh B, Suganthan PN (2016) Economic dispatch using hybrid grey wolf optimizer. Energy 111:630–641
Zhang S, Zhou Y, Li Z, Pan W (2016) Grey wolf optimizer for unmanned combat aerial vehicle path planning. Adv Eng Soft 99:121–136
Lu C, Gao L, Li X, Xiao S (2017) A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry. Eng Appl Artif Intell 57:61–79
Al-Aboody N, Al-Raweshidy H (2016) Grey wolf optimization-based energy-efficient routing protocol for heterogeneous wireless sensor networks. In: 2016 4th International symposium on computational and business intelligence (ISCBI). IEEE, pp 101–107
Sweidan AH, El-Bendary N, Hassanien AE, Hegazy OM, Mohamed AK (2016) Grey wolf optimizer and case-based reasoning model for water quality assessment. In: The 1st international conference on advanced intelligent system and informatics (AISI2015), November 28-30, 2015, Springer, Beni Suef, Egypt, pp 229–239
Li L, Sun L, Kang W, Guo J, Han C, Li S (2016) Fuzzy multilevel image thresholding based on modified discrete grey wolf optimizer and local information aggregation. IEEE Access 4:6438–6450
Gupta S, Deep K (2018) Random walk grey wolf optimizer for constrained engineering optimization problems. Comput Intell 34(4):1025–1045
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Method Appl Mech Eng 186(2–4):311–338
Rao SS (1983) Optimization theory and applications. Wiley, 605 THIRD AVE., NEW YORK, NY 10158, USA, 1983, 550
Deb K (2008) Introduction to evolutionary multiobjective optimization. In: Multiobjective optimization. Springer, pp 59–96
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Pratap Singh, A., Kumar Dixit, A. (2021). Grey Wolf Optimizer for Data Envelopment Analysis. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_15
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DOI: https://doi.org/10.1007/978-981-16-2712-5_15
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