International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 11, No. 4, 8 Aug. 2021
Cover page and Table of Contents: PDF (size: 1035KB)
An Improved Grey Wolf Optimization Algorithm for Liquid flow Control System
Full Text (PDF, 1035KB), PP.10-21
Views: 0 Downloads: 0
Author(s)
Pijush Dutta
1,*
Madhurima Majumder
2
Asok Kumar
3
Index Terms
Liquid flow process, Grey wolf optimizer, Particle swarm algorithm, hybrid algorithm.
Abstract
Liquid flow in a process industry is one of the significant factors which should be controlled to get the better quality and decrease the expense of generation. Customary methodology includes manual tuning of the input process parameter to obtain the required flow rate is tedious and exorbitant. Notwithstanding, estimation of a precise computational model for fluid stream control procedure can fill in as elective methodology. It is only a non-straight enhancement issue. As a contextual investigation, the WFT - 20-I measure control arrangement for flow rate measurement and Control issue is thought of. In this work we proposes a hybrid improved particle swarm optimization (PSO-GWO) used to start the people's position, which can build the decent variety of the wolf pack, balance the global and neighborhood search capacity of the calculation and improve the intermingling pace of the calculation contrast with the Gray wolf enhancement (GWO) and Particle swarm advancement (PSO). Non linear models are improved utilizing those recently proposed streamlining strategies. Additionally all the utilized optimization techniques can anticipate the fluid stream rate with good exactness. The outcomes were investigated by utilizing the root mean square error (RMSE), exactness, and the different measures to evaluate the level of identification performance of the liquid flow contextual analysis model. The trustworthiness of the present models was compared with the past model for similar subsystems utilizing competitive intelligent methodologies. The measurable examination of the acquired outcomes produced the proposed HPSOGWO has most elevated generally speaking proficiency (i.e.99.96%) and it beat the others strategies for the majority of the instances of demonstrating for fluid stream control process. The outcomes of the present model show that the proposed approach gives prevalent demonstrating execution and outflanks its rivals.
Cite This Paper
Pijush Dutta, Madhurima Majumder, Asok Kumar, " An Improved Grey Wolf Optimization Algorithm for Liquid flow Control System", International Journal of Engineering and Manufacturing (IJEM), Vol.11, No.4, pp. 10-21, 2021. DOI: 10.5815/ijem.2021.04.02
Reference
[1]Dutta P, Mandal S, Kumar A. Application of FPA and ANOVA in the optimization of liquid flow control process. RCES 2019;5:7–11. https://doi.org/10.18280/rces.050102.
[2]Dutta P, Mandal S, Kumar A. Comparative study: FPA based response surface methodology & ANOVA for the parameter optimization in process control. AMA_C 2018;73:23–7. https://doi.org/10.18280/ama_c.730104.
[3]Bera SC, Marik S. Study of a Simple Linearization Technique of p-n-Junction-Type Anemometric Flow Sensor 2012;61:545–52.
[4]Dutta P, Kumar A. Intelligent calibration technique using optimized fuzzy logic controller for ultrasonic flow sensor. MMEP 2017;4:91–4. https://doi.org/10.18280/mmep.040205.
[5]Ashhab M, Al-Salaymeh A. Optimization of hot-wire thermal flow sensor based on a neural net model, 2006. https://doi.org/10.1016/j.applthermaleng.2005.08.004.
[6]Al-Salaymeh A. Optimization of hot-wire thermal flow sensor based on a neural net model. Applied Thermal Engineering 2006;26:948–55.
[7]Santhosh KV, Roy BK. A practically validated intelligent calibration technique using optimized ANN for ultrasonic flow meter. International Journal on Electrical Engineering and Informatics 2015;7:379.
[8]Waller NG, Tellegen A, McDonald RP, Lykken DT. Exploring nonlinear models in personality assessment: Development and preliminary validation of a negative emotionality scale. Journal of Personality 1996;64:545–76.
[9]Dutta P, Mandal S, Kumar A. Comparative study: FPA based response surface methodlogy and ANOVA for the parameter Optimization in Process Control. Advances in Modelling and Analysis C 2018;73:23–7.
[10]Dutta P, Mandal S, Kumar A. Application of FPA and ANOVA in the optimization of liquid flow control process. RCES 2019;5:7–11. https://doi.org/10.18280/rces.050102.
[11]Amemiya T. Non-linear regression models. Handbook of Econometrics 1983;1:333–89.
[12]Dutta P, Kumar A. Design an intelligent flow measurement technique by optimized fuzzy logic controller, 2018. https://doi.org/10.3166/JESA.51.89-107.
[13]Dutta Pijush, Kumar Asok. Study of optimized NN model for liquid flow sensor based on different parameters. Proceeding of international conference on materials, applied physics and engineering, 2018.
[14]Dutta P, Kumar A. Flow sensor Ana- logue:Realtime Predic- tion Analysis using SVM & KNN, 2018.
[15]Dutta P, Kumar A. Design an intelligent calibration technique using optimized GA-ANN for liquid flow control system. Journal Européen Des Systèmes Automatisés 2017;50:449–70. https://doi.org/10.3166/jesa.50.449-470.
[16]Dutta Pijush, Kumar Asok. Application of an anfis model to optimize the liquid flow rate of a process control system. Chemical Engineering Transactions 2018;71:991–6. https://doi.org/10.3303/CET1871166.
[17]Mandal S, Dutta P, Kumar A. Modeling of liquid flow control process using improved versions of elephant swarm water search algorithm. SN Appl Sci 2019;1:886. https://doi.org/10.1007/s42452-019-0914-5.
[18]Amemiya T. Non-Linear Regression Models//Handbook of Econometrics. Vol. 1/Ed. by Zvi Griliches, Michael D. Intriligator. Amsterdam; New York: North-Holland Pub. Co., 1983. P. 333-389 1983.
[19]Gandomi AH, Yang X-S, Talatahari S, Alavi AH. Metaheuristic applications in structures and infrastructures. Newnes; 2013.
[20]Eberhart RC, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the 2000 congress on evolutionary computation. CEC00 (Cat. No. 00TH8512), vol. 1, IEEE; 2000, p. 84–8.
[21]Dutta P, Agarwala R, Majumder M, Kumar A. PARAMETERS EXTRACTION OF A SINGLE DIODE SOLAR CELL MODEL USING BAT ALGORITHM, FIREFLY ALGORITHM & CUCKOO SEARCH OPTIMIZATION. Annals of the Faculty of Engineering Hunedoara 2020;18:147–56.
[22]Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 2007;39:459–71.
[23]Yang X-S. Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation 2010;2:78–84.
[24]Yang X-S. Flower pollination algorithm for global optimization. International conference on unconventional computing and natural computation, Springer; 2012, p. 240–9.
[25]Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 1996;26:29–41.
[26]Yang X-S, Deb S. Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation 2010;1:330–43.
[27]Mandal S, Dutta P, Kumar A. Modeling of liquid flow control process using improved versions of elephant swarm water search algorithm. SN Applied Sciences 2019;1:1–16.
[28]Dutta P, Biswas SK, Biswas S, Majumder M. Parametric optimization of Solar Parabolic Collector using metaheuristic Optimization. Computational Intelligence and Machine Learning 2021;2:26–32.
[29]Onyezewe A, Kana AF, Abdullahi F, Abdulsalami A. An Enhanced Adaptive k-Nearest Neighbor Classifier Using Simulated Annealing. International Journal of Intelligent Systems and Applications 2021;13:34–44. https://doi.org/10.5815/ijisa.2021.01.03.
[30]Messaoudi A, Belkacemi M. Optimal Power Flow Solution using Efficient Sine Cosine Optimization Algorithm. International Journal of Intelligent Systems and Applications 2020;12:34–43. https://doi.org/10.5815/ijisa.2020.02.04.
[31]Sharma D, Dubey H, Pandit M. Single and Multi-Area Optimal Dispatch by Modified Salp Swarm Algorithm. International Journal of Intelligent Systems and Applications 2020;12:18–26. https://doi.org/10.5815/ijisa.2020.03.03.
[32]Wolpert DH. The Implications of the No-Free-Lunch Theorems for Meta-induction. ArXiv Preprint ArXiv:210311956 2021.
[33]Mirjalili S, Mirjalili SM, Lewis A. Grey Wolf Optimizer. Advances in Engineering Software 2014;69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007.
[34]Wen Long, Songjin Xu. A novel grey wolf optimizer for global optimization problems. 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2016, p. 1266–70. https://doi.org/10.1109/IMCEC.2016.7867415.
[35]Zhu A, Xu C, Li Z, Wu J, Liu Z. Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC. Journal of Systems Engineering and Electronics 2015;26:317–28. https://doi.org/10.1109/JSEE.2015.00037.
[36]Singh N, Singh S. A Modified Mean Gray Wolf Optimization Approach for Benchmark and Biomedical Problems. Evol Bioinform Online 2017;13:1176934317729413. https://doi.org/10.1177/1176934317729413.
[37]Li Q, Chen H, Huang H, Zhao X, Cai Z, Tong C, et al. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis. Computational and Mathematical Methods in Medicine 2017;2017:9512741. https://doi.org/10.1155/2017/9512741.
[38]Emary E, Zawbaa HM, Grosan C. Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks - IEEE Journals & Magazine. IEEE Transactions on Neural Networks and Learning Systems, 2017.
[39]Zhang S, Zhou Y. Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis. Discrete Dynamics in Nature and Society 2015;2015:481360. https://doi.org/10.1155/2015/481360.
[40]Dutta P, Kumar A. Modeling and Optimization of a Liquid Flow Process using an Artificial Neural Network-Based Flower Pollination Algorithm. Journal of Intelligent Systems 2019;29:787–98. https://doi.org/10.1515/jisys-2018-0206.
[41]Dutta P, Kumar A. Modelling of Liquid Flow control system Using Optimized Genetic Algorithm. Statistics, Optimization & Information Computing 2020;8:565–82. https://doi.org/10.19139/soic-2310-5070-618.
[42]Dutta P, Kumar A. Modelling of Liquid Flow control system Using Optimized Genetic Algorithm. Statistics, Optimization & Information Computing 2020;8:565–82.
[43]Glantz S, Slinker B, Neilands TB. Primer of Applied Regression & Analysis of Variance 3E. 2016.
[44]Şenel FA, Gökçe F, Yüksel AS, Yiğit T. A novel hybrid PSO–GWO algorithm for optimization problems. Engineering with Computers 2019;35:1359–73.
[45]Mishra AK, Das SR, Ray PK, Mallick RK, Mohanty A, Mishra DK. PSO-GWO Optimized Fractional Order PID Based Hybrid Shunt Active Power Filter for Power Quality Improvements. IEEE Access 2020;8:74497–512.
[46]Xiang Z, Ji D, Zhang H, Wu H, Li Y. A simple PID-based strategy for particle swarm optimization algorithm. Information Sciences 2019;502:558–74.
[47]Sangeetha AL, Bharathi N, Ganesh AB, Radhakrishnan TK. Particle swarm optimization tuned cascade control system in an Internet of Things (IoT) environment. Measurement 2018;117:80–9.
[48]Wang D, Tan D, Liu L. Particle swarm optimization algorithm: an overview. Soft Computing 2018;22:387–408.