Abstract
Motivated by the increasing complexity and operational productivity of industrial processes, the need for efficient modeling schemes for industrial systems is highly demanded. This study presents a new simulator model for a real winding process based on a combination of Multi-gene Genetic Programming (MGP) and Capuchin Search Algorithm (CapSA), referred to as MGP-CapSA modeling approach. CapSA is a meta-heuristic algorithm used to optimize the coefficients of the regression equations of the MGP technique. The winding process has tensions in the web between reels 1 and 2 and between reels 2 and 3. On this basis, two mathematical models were developed by the MGP-CapSA method to estimate the tensions in the web for this process. The efficacy and superiority of the proposed MGP-CapSA method were verified by extensive experiments and hypothesis testing, and the proposed method was then compared with other well-known intelligent and conventional modeling methods. The proposed MGP-CapSA method can be exploited to enhance control performance and achieve robust fault-tolerant system. A comparison of the MGP-CapSA method with other promising modeling methods corroborates the performance level of MGP-CapSA over those competitors. The results demonstrate that MGP-CapSA is a suitable method for generating robust models for complex nonlinear systems.
Similar content being viewed by others
References
Sheta A, Braik M, Al-Hiary H (2019) Modeling the tennessee eastman chemical process reactor using bio-inspired feedforward neural network (bi-ff-nn). Int J Adv Manuf Technol 103(1):1359–1380
Sheta AF, Braik M, Öznergiz E, Ayesh A, Masud M (2013) Design and automation for manufacturing processes an intelligent business modeling using adaptive neuro-fuzzy inference systems. Business Intelligence and Performance Management. Springer, Berlin, pp 191–208
Nozari HA, Banadaki HD, Mokhtare M, Vahed SH (2012) Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks. J Zhejiang Univ Sci C 13(6):403–412
Braik M, Al-Zoubi H, Al-Hiary H (2020) Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study. Soft Comput 25(6):4545–4569
Babuška R, Verbruggen H (2003) Neuro-fuzzy methods for nonlinear system identification. Annual Rev Control 27(1):73–85
Braatz RD, Ogunnaike BA, Featherstone AP (1996) Identification, estimation, and control of sheet and film processes. IFAC Proceed Vol 29(1):6638–6643
Torres PJR, Mercado EIS, Rifón LA (2018) Probabilistic boolean network modeling of an industrial machine. J Intell Manuf 29(4):875–890
Ogunjuyigbe ASO, Ayodele TR, Adetokun BB (2018) Modelling and analysis of dual stator-winding induction machine using complex vector approach. Eng Sci Technol, Int J 21(3):351–363
Cross P, Ma X (2014) Nonlinear system identification for model-based condition monitoring of wind turbines. Renewable Energy 71:166–175
Hussian A, Sheta A, Kamel M, Telbaney M, Abdelwahab A (2000) Modeling of a winding machine using genetic programming. In Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), volume 1, pages 398–402. IEEE
Sheta AF, Faris H, Öznergiz E (2014) Improving production quality of a hot-rolling industrial process via genetic programming model. Int J Comput Appl Technol 49(3–4):239–250
Pradyut Kumar Muduli and Sarat Kumar Das (2015) Model uncertainty of spt-based method for evaluation of seismic soil liquefaction potential using multi-gene genetic programming. Soils Found 55(2):258–275
Faris H, Sheta AF, Öznergiz E (2016) Mgp-cc: a hybrid multigene gp-cuckoo search method for hot rolling manufacture process modelling. Syst Sci Control Eng 4(1):39–49
Noura H, Theilliol D, Ponsart J-C and Chamseddine A (2009) Design and practical applications. Springer Science & Business Media, Fault-tolerant control systems
Chu X, Nian X, Liu J, Liao Y (2017) Robust fault detection for multi-motor winding system based on disturbance observer and sliding-mode observer. In 2017 11th Asian Control Conference (ASCC), pages 1519–1524. IEEE
Puig V, Quevedo J (2001) Fault-tolerant pid controllers using a passive robust fault diagnosis approach. Control Eng Practice 9(11):1221–1234
Mohammad Ghasem Kazemi and Mohsen Montazeri (2017) A new robust fault diagnosis approach based on bond graph method. J Brazilian Soc Mech Sci Eng 39(11):4353–4365
Chu X, Nian X, Xinran F, Wang H, Xiong H (2020) Modeling and robust decentralized control for speed-up phase of web processing systems for composite elastic web. J Franklin Inst 357(11):6694–6720
Fang H, Meng F, Yan J, Chen G, Zhang L, Shide W, Zhang S, Wang L, Zhang Y (2019) Fe 3 o 4 hard templating to assemble highly wrinkled graphene sheets into hierarchical porous film for compact capacitive energy storage. RSC Adv 9(35):20107–20112
Shah PH, Badheka VJ (2019) Friction stir welding of aluminium alloys: An overview of experimental findings-process, variables, development and applications. Proceed Inst Mech Eng, Part L: J Mater: Design Appl 233(6):1191–1226
Mele M, Magazzino C (2020) A machine learning analysis of the relationship among iron and steel industries, air pollution, and economic growth in china. J Clean Prod 277:123293
Tahmassebi A, Gandomi AH (2018) Building energy consumption forecast using multi-objective genetic programming. Measurement 118:164–171
Choi S, Haque MS, Tarek MTB, Mulpuri V, Duan Y, Das S, Garg V, Ionel DM, Masrur MA, Mirafzal B et al (2018) Fault diagnosis techniques for permanent magnet ac machine and drives–a review of current state of the art. IEEE Trans Transp Electr 4(2):444–463
Chen J, Patton RJ (2012) Robust model-based fault diagnosis for dynamic systems. Springer Science & Business Media, Berlin
Braik M, Sheta A, Al-Hiary H (2020) A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm. Neural Comput Appl 33(7):2515–2547
Koza JR, Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT press, Cambridge
Hussian A, Sheta A, Kamel M, Telbaney M, Abdelwahab A (2000) Modeling of a winding machine using genetic programming. In Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, volume 1, pp. 398–402. IEEE
Sheta A, Aljahdali S, Braik M (2018) Utilizing faults and time to finish estimating the number of software test workers using artificial neural networks and genetic programming. In International Conference Europe Middle East & North Africa Information Systems and Technologies to Support Learning, pp. 613–624. Springer
Faris H, Sheta A (2013) Identification of the tennessee eastman chemical process reactor using genetic programming. Int J Adv Sci Technol 50:121–140
Alonso CL, Montaña JL, Borges CE (2009) Evolution strategies for constants optimization in genetic programming. In 2009 21st IEEE International Conference on Tools with Artificial Intelligence, pp. 703–707. IEEE
Mukherjee S, Eppstein MJ (2012) Differential evolution of constants in genetic programming improves efficacy and bloat. In Proceedings of the 14th annual conference companion on Genetic and evolutionary computation, pp. 625–626
Bastogne T, Noura H, Sibille P, Richard A (1998) Multivariable identification of a winding process by subspace methods for tension control. Control Eng Practice 6(9):1077–1088
Heddam S, Kisi O (2018) Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and m5 model tree. J Hydrol 559:499–509
Mosavi A, Shirzadi A, Choubin B, Taromideh F, Hosseini FS, Borji M, Shahabi H, Salvati A, Dineva AA (2020) Towards an ensemble machine learning model of random subspace based functional tree classifier for snow avalanche susceptibility mapping. IEEE Access 8:145968–145983
Xu S, Lu B, Baldea M, Edgar TF, Wojsznis W, Blevins T, Nixon M (2015) Data cleaning in the process industries. Rev Chem Eng 31(5):453–490
Jung K, Bae D-H, Um M-J, Kim S, Jeon S, Park D (2020) Evaluation of nitrate load estimations using neural networks and canonical correlation analysis with k-fold cross-validation. Sustainability 12(1):400
Gandomi AH, Sajedi S, Kiani B, Huang Q (2016) Genetic programming for experimental big data mining: A case study on concrete creep formulation. Autom Constr 70:89–97
Soleimani S, Rajaei S, Jiao P, Sabz A, Soheilinia S (2018) New prediction models for unconfined compressive strength of geopolymer stabilized soil using multi-gen genetic programming. Measurement 113:99–107
Babuska R (1998) Fuzzy modeling and identification toolbox. Delft University of Technology, The Netherland, http://lcewww.et.tudelft.nl/bubuska, 204
Legg S, Hutter M, Kumar A (2004) Tournament versus fitness uniform selection. In Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), vol. 2, pp. 2144–2151. IEEE
Searson DP, Leahy DE, Willis MJ (2010) Gptips: an open source genetic programming toolbox for multigene symbolic regression. In Proceedings of the International multiconference of engineers and computer scientists, vol. 1, pp. 77–80. IMECS Hong Kong
Garg A, Tai K (2014) An improved multi-gene genetic programming approach for the evolution of generalized model in modelling of rapid prototyping process. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 218–226. Springer
Słowik A, Białko M (2008) Design and multi-objective optimization of combinational digital circuits using evolutionary algorithm with multi-layer chromosomes. In International Conference on Artificial Intelligence and Soft Computing, pages 479–488. Springer
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191
Madár J, Abonyi J, Szeifert F (2005) Genetic programming for the identification of nonlinear input- output models. Ind Engineering Chem Res 44(9):3178–3186
Searson DP (2015) Gptips 2: an open-source software platform for symbolic data mining. In Handbook of genetic programming applications, pp. 551–573. Springer
Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evolut Comput 6(2):182–197
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
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
Braik, M. A Hybrid Multi-gene Genetic Programming with Capuchin Search Algorithm for Modeling a Nonlinear Challenge Problem: Modeling Industrial Winding Process, Case Study. Neural Process Lett 53, 2873–2916 (2021). https://doi.org/10.1007/s11063-021-10530-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-021-10530-w