Information Technology for Designing Rule bases of Fuzzy Systems using Ant Colony Optimization

Authors

  • Oleksiy Kozlov

DOI:

https://doi.org/10.47839/ijc.20.4.2434

Keywords:

fuzzy system, designing rule base, information technology, bioinspired multi-agent techniques, ant colony optimization, adaptive control system, ship steering device

Abstract

This paper proposes the universal information technology for designing the rule bases (RB) with the formation of optimal consequents for fuzzy systems (FS) of different types on the basis of ant colony optimization (ACO) techniques. The developed ACO-based information technology allows effectively synthesizing rule bases of various dimensions both for the MISO and MIMO fuzzy systems taking into account the particular features of the RB consequents formation in the conditions of insufficient initial information. In order to study and validate the efficiency of the presented information technology the design of the RB for the adaptive fuzzy control system of the ship steering device is carried out in this work. The computer simulations results show that adaptive control system with developed RB provides achievement of high enough quality indicators of rudder angle control. Thus, application of the proposed ACO-based information technology allows designing effective RB with optimal consequents by means of minor computational costs that, in turn, confirms its high efficiency.

References

L. A. Zadeh, A. M. Abbasov, R. R. Yager, S. N. Shahbazova, M. Z. Reformat, Eds., “Recent developments and new directions in soft computing,” STUDFUZ 317, Cham: Springer, 2014, 466 p. https://doi.org/10.1007/978-3-319-06323-2.

Y. P. Kondratenko, O. V. Korobko, O. V. Kozlov, “Synthesis and optimization of fuzzy controller for thermoacoustic plant,” Lotfi A. Zadeh et al. (Eds.), Recent Developments and New Direction in Soft-Computing Foundations and Applications, Studies in Fuzziness and Soft Computing, vol. 342, Berlin, Heidelberg: Springer-Verlag, 2016, pp. 453-467. https://doi.org/10.1007/978-3-319-32229-2_31.

M. Jamshidi, V. Kreinovich, J. Kacprzyk, Eds., “Advance Trends in Soft Computing,” STUDFUZ 312, Cham: Springer-Verlag, 2013, 468 p. https://doi.org/10.1007/978-3-319-03674-8.

L. A. Zadeh, “The role of fuzzy logic in modeling, identification and control,” Modeling Identification and Control, vol. 15, issue 3, pp. 191–203, 1994. https://doi.org/10.4173/mic.1994.3.9.

E. H. Mamdani, “Application of fuzzy algorithms for control of simple dynamic plant,” Proceedings of IEEE, vol. 121, pp. 1585-1588, 1974. https://doi.org/10.1049/piee.1974.0328.

B. Kosko, “Fuzzy systems as universal approximators,” IEEE Transactions on Computers, vol. 43, no. 11, pp. 1329-1333, 1994. https://doi.org/10.1109/12.324566.

Y. P. Kondratenko, O. V. Korobko, O. V. Kozlov, “Frequency tuning algorithm for loudspeaker driven thermoacoustic refrigerator optimization,” Lecture Notes in Business Information Processing: Modeling and Simulation in Engineering, Economics and Management. – K. J. Engemann, A. M. Gil-Lafuente, J. M. Merigo (Eds.), vol. 115, Berlin, Heidelberg: Springer-Verlag, 2012, pp. 270–279. https://doi.org/10.1007/978-3-642-30433-0_27.

V. M. Kuntsevich, et al. (Eds)., Control Systems: Theory and Applications, Series in Automation, Control and Robotics, River Publishers, 2018.

Y. P. Kondratenko, O. V. Kozlov, “Mathematic modeling of reactor’s temperature mode of multiloop pyrolysis plant,” Modeling and Simulation in Engineering, Economics and Management, Lecture Notes in Business Information Processing, vol. 115, 2012, pp. 178-187. https://doi.org/10.1007/978-3-642-30433-0_18.

T. Bora, P. Chatterjee, S. Ghosh, “Fuzzy logic based control of variable wind energy system,” Proceedings of the 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, India, pp. 1-5, 2020. https://doi.org/10.1109/ICRAIE51050.2020.9358376.

A. Piegat, Fuzzy Modeling and Control, Springer, Heidelberg, 2001, 728 p. https://doi.org/10.1007/978-3-7908-1824-6.

V. Uslan, H. Seker, R. John, “Overlapping clusters and support vector machines based interval type-2 fuzzy system for the prediction of peptide binding affinity,” IEEE Access, vol. 7, pp. 49756-49764, 2019. https://doi.org/10.1109/ACCESS.2019.2910078.

A. Jain, M. B. Jain, “Fuzzy modeling and similarity based short term load forecasting using evolutionary particle swarm optimization,” Proceedings of the 2012 IEEE Power and Energy Society General Meeting, 2012, pp. 1-1. https://doi.org/10.1109/PESGM.2012.6345769.

J. M. Mendel, Uncertain Rule-based Fuzzy Systems, Introduction and New Directions, Second Edition, Springer International Publishing, 2017, 684 p. https://doi.org/10.1007/978-3-319-51370-6.

W. A. Lodwick, J. Kacprzhyk, Eds., “Fuzzy optimization,” STUDFUZ, vol. 254, Berlin, Heidelberg: Springer-Verlag, 2010.

O. Kosheleva, V. Kreinovich, “Why Bellman-Zadeh approach to fuzzy optimization,” Appl. Math. Sci., vol. 12, pp. 517-522, 2018. https://doi.org/10.12988/ams.2018.8456.

G. M. Méndez, P. N. M. Dorantes, A. M. Santoyo, “Interval type-2 fuzzy logic systems optimized by central composite design to create a simplified fuzzy rule base in image processing for quality control application,” The International Journal of Advanced Manufacturing Technology, vol. 102, no. 9-12, pp. 3757-3766, 2019. https://doi.org/10.1007/s00170-019-03354-5.

D. Simon, Evolutionary Optimization Algorithms: Biologically Inspired and Population-based Approaches to Computer Intelligence, John Wiley & Sons, 2013, 772 p.

A. Melendez, O. Castillo, “Evolutionary optimization of the fuzzy integrator in a navigation system for a mobile robot,” Recent Advances on Hybrid Intelligent Systems, pp. 21-31, 2013. https://doi.org/10.1007/978-3-642-33021-6_2.

J. Zhu, F. Lauri, A. Koukam, V. Hilaire, “Fuzzy logic control optimized by artificial immune system for building thermal condition,” Siarry P., Idoumghar L., Lepagnot J. (eds) Swarm Intelligence Based Optimization, ICSIBO 2014, Lecture Notes in Computer Science, vol. 8472, Springer, Cham, 2014, pp. 42-49. https://doi.org/10.1007/978-3-319-12970-9_5.

I. Boussaïd, J. Lepagnot, P. Siarry, “A survey on optimization metaheuristics,” Information Science, vol. 237, pp. 82-117, 2013. https://doi.org/10.1016/j.ins.2013.02.041.

A. Gogna, A. Tayal. “Metaheuristics: review and application,” Journal of Experimental and Theoretical Artificial Intelligence, vol. 25, pp. 503-526, 2013. https://doi.org/10.1080/0952813X.2013.782347.

C. Blum, J. Puchinger, G. R. Raidl, A. Roli, “Hybrid metaheuristics in combinatorial optimization: a survey,” Applied Soft Computing, vol. 11, pp. 4135-4151, 2011. https://doi.org/10.1016/j.asoc.2011.02.032.

T. Takagi, M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man, and Cybernetics, SMC–15, no. 1, pp. 116-132, 1985. https://doi.org/10.1109/TSMC.1985.6313399.

R.-C. Roman, R.-E. Precup, C.-A. Bojan-Dragos, A.-I. Szedlak-Stinean, “Combined model-free adaptive control with fuzzy component by virtual reference feedback tuning for tower crane systems,” Procedia Computer Science, vol. 162, pp. 267-274, 2019. https://doi.org/10.1016/j.procs.2019.11.284.

Z. Xiao, J. Guo, H. Zeng, P. Zhou, S. Wang, “Application of fuzzy neural network controller in hydropower generator unit,” J. Kybernetes, vol. 38, no. 10, pp. 1709-1717, 2009. https://doi.org/10.1108/03684920910994079.

M. I. Fadholi, Suhartono, P. S. Sasongko, Sutikno, “Autonomous pole balancing design in quadcopter using behaviour-based intelligent fuzzy control,” Proceedings of the 2018 2nd International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia, pp. 1-6, 2018. https://doi.org/10.1109/ICICOS.2018.8621736.

Y. P. Kondratenko, O. V. Kozlov, “Mathematical model of ecopyrogenesis reactor with fuzzy parametrical identification,” Recent Developments and New Direction in Soft-Computing Foundations and Applications, Studies in Fuzziness and Soft Computing, Vol. 342, Lotfi A. Zadeh et al. (Eds.). Berlin, Heidelberg: Springer-Verlag, 2016, pp. 439-451. https://doi.org/10.1007/978-3-319-32229-2_30.

Y. P. Kondratenko, A. V. Kozlov, “Generation of rule bases of fuzzy systems based on modified ant colony algorithms,” Journal of Automation and Information Sciences, vol. 51, issue 3, New York: Begel House Inc., pp. 4-25, 2019. https://doi.org/10.1615/JAutomatInfScien.v51.i3.20.

G. Ruiz-García, H. Hagras, H. Pomares, I. R. Ruiz, “Toward a fuzzy logic system based on general forms of interval type-2 fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol. 27, no. 12, pp. 2381-2395, 2019. https://doi.org/10.1109/TFUZZ.2019.2898582.

P. Hajek, V. Olej, “Interval-valued intuitionistic fuzzy inference system for supporting corporate financial decisions,” Proceedings of the 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018, pp. 1-7. https://doi.org/10.1109/FUZZ-IEEE.2018.8491620.

D. Driankov, H. Hellendoorn, M. Reinfrank, An Introduction to fuzzy Control, Springer Science & Business Media, 2013.

Y. P. Kondratenko, A. V. Kozlov, “Parametric optimization of fuzzy control systems based on hybrid particle swarm algorithms with elite strategy,” Journal of Automation and Information Sciences, vol. 51, issue 12, New pp. 25-45, 2019. https://doi.org/10.1615/JAutomatInfScien.v51.i12.40.

S. Khan, et al., “Design and implementation of an optimal fuzzy logic controller using genetic algorithm,” Journal of Computer Science, vol. 4, no. 10, pp. 799-806, 2008. https://doi.org/10.3844/jcssp.2008.799.806.

D. Simon, “H∞ estimation for fuzzy membership function optimization,” International Journal of Approximate Reasoning, 40, pp. 224-242, 2005. https://doi.org/10.1016/j.ijar.2005.04.002.

F. Valdez, P. Melin and O. Castillo, “Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making,” Proceedings of the 2009 IEEE International Conference on Fuzzy Systems, 2009, pp. 2114-2119. https://doi.org/10.1109/FUZZY.2009.5277165.

O. Kozlov, G. Kondratenko, Z. Gomolka, Y. Kondratenko, “Synthesis and optimization of green fuzzy controllers for the reactors of the specialized pyrolysis plants,” Kharchenko V., Kondratenko Y., Kacprzyk J. (eds), Green IT Engineering: Social, Business and Industrial Applications, Studies in Systems, Decision and Control, vol 171, Springer, Cham, 2019, pp. 373-396. https://doi.org/10.1007/978-3-030-00253-4_16.

H. Ishibuchi, T. Yamamoto, “Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining,” Fuzzy Sets and Systems, vol. 141, no. 1, pp. 59-88, 2004. https://doi.org/10.1016/S0165-0114(03)00114-3.

B. Jayaram, “Rule reduction for efficient inferencing in similarity based reasoning,” International Journal of Approximate Reasoning, vol. 48, no. 1, pp. 156-173, 2008. https://doi.org/10.1016/j.ijar.2007.07.009.

P. C. Shill, Y. Maeda, K. Murase, “Optimization of fuzzy logic controllers with rule base size reduction using genetic algorithms,” Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA), Singapore, 2013, pp. 57-64. https://doi.org/10.1109/CICA.2013.6611664.

C. Li, H. Zhao, S. Zhen, Y. -H. Chen, “Control design with optimization for fuzzy steering-by-wire system based on nash game theory,” IEEE Transactions on Cybernetics, pp. 1-10, 2021. https://doi.org/10.1109/TCYB.2021.3050509.

L. D. Seixas, H. G. Tosso, F. C. Corrêa, J. J. Eckert, “Particle swarm optimization of a fuzzy controlled hybrid energy storage system – HESS,” Proceedings of the 2020 IEEE Vehicle Power and Propulsion Conference (VPPC), Gijon, Spain, pp. 1-6. 2020. https://doi.org/10.1109/VPPC49601.2020.9330939.

C. Juang, C. Hsiao and C. Hsu, “Hierarchical cluster-based multispecies particle-swarm optimization for fuzzy-system optimization,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 1, pp. 14-26, 2010. https://doi.org/10.1109/TFUZZ.2009.2034529

W. Pedrycz, K. Li, M. Reformat, “Evolutionary reduction of fuzzy rule-based models,” Fifty Years of Fuzzy Logic and its Applications, STUDFUZ, vol. 326, 2015, Cham: Springer, pp. 459-481. https://doi.org/10.1007/978-3-319-19683-1_23.

R. Alcalá, J. Alcalá-Fdez, M. J. Gacto, F. Herrera, “Rule base reduction and genetic tuning of fuzzy systems based on the linguistic 3-tuples representation,” Soft Computing, vol. 11, no. 5, pp. 401-419, 2007. https://doi.org/10.1007/s00500-006-0106-2.

A. Nabi, N.A. Singh, “Particle swarm optimization of fuzzy logic controller for voltage sag improvement,” Proceedings of 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 01, 2016, pp. 1-5. https://doi.org/10.1109/ICACCS.2016.7586345.

M. Dorigo, M. Birattari, Ant Colony Optimization, Encyclopedia of Machine Learning, Sammut C., Webb G.I. (eds.), Springer, Boston, MA, 2011. https://doi.org/10.1007/978-0-387-30164-8_22.

N. Quijano, K. M. Passino, Honey Bee Social Foraging Algorithms for Resource Allocation: Theory and Application, Columbus: Publishing house of the Ohio State University, 39 p., 2007. https://doi.org/10.1109/ACC.2007.4282168.

S. Vaneshani, H. Jazayeri-Rad, “Optimized fuzzy control by particle swarm optimization technique for control of CSTR,” International Journal of Electrical and Computer Engineering, vol. 5, no. 11, pp. 1243-1248, 2011.

D. H. Kim, C. H. Cho, “Bacterial foraging based neural network fuzzy learning,” Proceedings of the 2nd Indian International Conference on Artificial Intelligence (IICAI’2005), Pune, pp. 2030-2036, 2005.

L. Fan, E. M. Joo, “Design for auto‐tuning PID controller based on genetic algorithms,” Proceedings of the 4th IEEE Conference on Industrial Electronics and Applications ICIEA’2009, 2009, pp. 1924-1928. https://doi.org/10.1109/ICIEA.2009.5138538.

F. L. Minku, T. Ludermir, “Evolutionary strategies and genetic algorithms for dynamic parameter optimization of evolving fuzzy neural networks,” Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 3, 2005, pp. 1951-1958.

O. Castillo, P. Ochoa, J. Soria, “Differential evolution with fuzzy logic for dynamic adaptation of parameters in mathematical function optimization,” Angelov P., Sotirov S. (eds), Imprecision and Uncertainty in Information Representation and Processing. Studies in Fuzziness and Soft Computing, vol. 332. Springer, Cham, 2016, pp. 361-374. https://doi.org/10.1007/978-3-319-26302-1_21.

D. Simon, “Biogeography-based optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, issue 6, pp. 702-713, 2008. https://doi.org/10.1109/TEVC.2008.919004.

R. T. Alves, M. R. Delgado, H. S. Lopes, A. A. Freitas, “An artificial immune system for fuzzy-rule induction in data mining,” Yao X. et al. (eds), Parallel Problem Solving from Nature – PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol. 3242. Springer, Berlin, Heidelberg, 2004, pp. 1011-1020. https://doi.org/10.1007/978-3-540-30217-9_102.

J.-J. Qin, X.-X. Zhang, G.-T. Cao, “Particle swarm optimization based scaling factor tuning for 3-D fuzzy logic controller,” Proceedings of the 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010, pp. 711-715. https://doi.org/10.1109/FSKD.2010.5569388.

Y. P. Kondratenko, O. V. Kozlov, O. V. Korobko, “Two modifications of the automatic rule base synthesis for fuzzy control and decision making systems,” J. Medina et al. (Eds), Information Processing and Management of Uncertainty in Knowledge-Based Systems: Theory and Foundations, 17th International Conference, IPMU 2018, Cadiz, Spain, Proceedings, Part II, CCIS 854, Springer International Publishing AG, 2018, pp. 570-582. https://doi.org/10.1007/978-3-319-91476-3_47.

R. Putha, L. Quadrifoglio, E. Zechman, “Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions,” Comput. Aided Civ. Infrastruct. Eng., vol. 27, pp. 14-28, 2012. https://doi.org/10.1111/j.1467-8667.2010.00715.x.

R. Gan, Q. Guo, H. Chang, Y. Yi, “Improved ant colony optimization algorithm for the traveling salesman problems,” Journal of Systems Engineering and Electronics, pp. 329-333, 2010. https://doi.org/10.3969/j.issn.1004-4132.2010.02.025.

R.-M. Chen, Y.-M. Shen, C.-T. Wang, “Ant colony optimization inspired swarm optimization for grid task scheduling,” Proceedings of the 2016 International Symposium on Computer, Consumer and Control (IS3C), 2016, pp. 461-464. https://doi.org/10.1109/IS3C.2016.122.

Q. Chengming, “Vehicle routing optimization in logistics distribution using hybrid ant colony algorithm,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 11, no. 9, pp. 5308-5315, 2013. https://doi.org/10.11591/telkomnika.v11i9.3284.

B. Benhala, A. Ahaitouf, M. Fakhfakh, A. Mechaqrane, “New adaptation of the ACO algorithm for the analog circuits design optimization,” International Journal of Computer Science (IJCSI), vol. 9, no. 3, pp. 360-367, 2012.

Y. Khaluf, S. Gullipalli, “An efficient ant colony system for edge detection in image processing,” Proceedings of the European Conference on Artificial Life, pp. 398-405, 2015.

C.-F. Juang, H.-J. Huang, C.-M. Lu, “Fuzzy controller design by ant colony optimization,” Proceedings of the 2007 IEEE International Fuzzy Systems Conference, London, UK, 2007, pp. 1-5. https://doi.org/10.1109/FUZZY.2007.4295335.

X. Yalang, S. Shiyu and H. Xin, “Optimization design of fuzzy controller based on improved ant colony algorithm,” Proceedings of the 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control, Beijing, 2011, pp. 875-878. https://doi.org/10.1109/IMCCC.2011.221.

G. Wei, Z. Xian-ku and W. Xin-ping, “Concise nonlinear adaptive robust control of ship steering systems,” Proceedings of the 2009 IEEE International Conference on Automation and Logistics, Shenyang, 2009, pp. 343-346. https://doi.org/10.1109/ICAL.2009.5262902.

J. Zhao, Y. Fu, J. Fu and G. Liu, “Fuzzy CMAC compound control of hydraulic servo actuation for ship steering system,” Proceedings of the 2017 IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, 2017, pp. 1792-1797. https://doi.org/10.1109/ICMA.2017.8016089.

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Published

2021-12-31

How to Cite

Kozlov, O. (2021). Information Technology for Designing Rule bases of Fuzzy Systems using Ant Colony Optimization. International Journal of Computing, 20(4), 471-486. https://doi.org/10.47839/ijc.20.4.2434

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