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22-09-2016 | Methodologies and Application

A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers

Authors: Leticia Amador-Angulo, Oscar Castillo

Published in: Soft Computing | Issue 2/2018

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Abstract

This paper presents a new fuzzy bee colony optimization method to find the optimal distribution of the membership functions in the design of fuzzy controllers for complex nonlinear plants. We used interval type-2 and type-1 fuzzy logic systems in dynamically adapting the alpha and beta parameter values of the bee colony optimization algorithm (BCO). Simulation results with a type-1 fuzzy logic controller for benchmark control plants are presented. The advantage of using interval type-2 fuzzy logic systems for dynamic adjustment of parameters in BCO applied in fuzzy controller design is verified with two benchmark problems. We considered different levels and types of noise in the simulations to analyze the approach of interval type-2 fuzzy logic systems to find the best values of alpha and beta for BCO when applied in the design of fuzzy controllers.

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Amador-Angulo L, Castillo O (2013a) Comparison of fuzzy controllers for the water tank with type-1 and type-2 fuzzy logic. In: NAFIPS 2013, Edmonton, Canada, pp 1–6

Amador-Angulo L, Castillo O (2013b) Comparison of the optimal design of fuzzy controllers for the water tank using ant colony optimization. In: Recent advances on hybrid approaches for designing intelligent systems, vol 547, Tijuana, B.C., pp 255–273

Amador-Angulo L, Castillo O (2015) A new algorithm based in the smart behavior of the bees for the design of Mamdani-style fuzzy controllers using complex non-linear plants. In: Design of intelligent systems based on fuzzy logic, neural network and nature-inspired, optimization, pp 617–637

Arabshahi P, Choi JJ, Marks RJ II, Caudell TP (1996) Fuzzy parameter adaptation in optimization: some neural net training examples. Comput Sci Eng 1:57–65CrossRef

Astudillo L, Castillo O, Aguilar L (2007) Intelligent control for a perturbed autonomous wheeled mobile robot: a type-2 fuzzy logic approach. Nonlinear Stud 14(1):37–48

Aung N, Cooper E, Hoshino Y, Kamei K (2007) A proposal of fuzzy control systems for trailers driven by multiple motors in side slipways to haul out ships. Int J Innov Comput Inf Control 3(4):799–812

Bel A, Wagner C, Hagras H (2013) Multiobjective optimization and comparison of nonsingleton type-1 and singleton interval type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 21(3):459–476CrossRef

Biesmeijer JC, Seeley TD (2005) The use of waggle dance information by honey bees throughout their foraging careers. Behav Ecol Sociobiol 59(1):133–142CrossRef

Bonabeau E, Dorigo M, Theraulaz G (1997) Swarm intelligence. Oxford University Press, OxfordMATH

Castillo O (2012) Interval type-2 Mamdani fuzzy systems for intelligent control, combining experimentation and theory. Springer, Berlin

Castillo O, Melin P (2008) Type-2 fuzzy logic theory and applications. Springer, BerlinMATH

Castillo O, Melin P (2012) A review on the design and optimization of interval type-2 fuzzy controllers. Appl Soft Comput (ASC) 12:1267–1278CrossRef

Castillo O, Martinez R, Melin P (2010) Bio-inspired optimization of fuzzy logic controllers for robotic autonomous system with PSO and ACO. In: Fuzzy information engineering, pp 119–143

Castillo O, Martinez-Marroquin R, Melin P, Valdez F, Soria J (2012) Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot. Inf Sci 192:19–38CrossRef

Castro JR, Castillo O, Melin P (2007) “An interval type-2 fuzzy logic toolbox for control applications. In: Fuzzy system conference, pp 1–6

Cázarez-Castro N, Aguilar LT, Castillo O (2012) Designing type-1 and type-2 fuzzy logic controllers via fuzzy Lyapunov synthesis for nonsmooth mechanical systems. Eng Appl Artif Intell 25:971–979CrossRef

Cervantes L, Castillo O, Melin P (2011) Intelligent control of nonlinear dynamic plants using a hierarchical modular approach and type-2 fuzzy logic. In: MICAI, pp 1–12

Chaiyatham T, Ngamroo I (2012) A bee colony optimization based-fuzzy logic-PID control design of electrolyzer for microgrid stabilization. Int J Innov Comput Inf Control 8(9):6049–6066

Chang WJ, Hsu FL (2015) Mamdani and Takagi-Sugeno fuzzy controller design for ship fin stabilizing systems. In: Fuzzy systems and knowledge discovery (FSKD), 2015 12th international conference on, pp 345–350. IEEE

Chatterjee A, Siarry P (2006) Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput Oper Res 33(3):859–871CrossRefMATH

Chen S, Chang Y, Pan J (2013) Fuzzy rules interpolation for sparse fuzzy rule-based systems based on interval type-2 gaussian fuzzy sets and genetic algorithms. IEEE Trans Fuzzy Syst 21(3):412–425CrossRef

Chong Ch, Low M, Sivakumar AK, Gay (2006) A bee colony optimization algorithm to job shop scheduling. In: Proceedings of the 2006 winter simulation conference, pp 1959

Dyler FC (2002) The biology of the dance language. Annu Rev Entomol 47:917–949CrossRef

Hagras HA (2007) Type-2 FLC: a new generalization of fuzzy controllers. In: IEEE computational intelligence magazine, February, pp 30–43

Hsu C, Juang C (2013) Evolutionary robot wall-following control using type-2 fuzzy controller with species-de-activated continuous aco. IEEE Trans Fuzzy Syst 21(1):100–112CrossRef

Juang CF, Lu CM, Lo C, Wang CY (2008) Ant colony optimization algorithm for fuzzy controller design and its fpga implementation. Ind Electron IEEE Trans 55(3):1453–1462CrossRef

Karnik NN, Mendel JM (2001) Operations on type-2 fuzzy sets. Int J Fuzzy Sets Syst 122:327–348MathSciNetCrossRefMATH

Karnik NN, Mendel JM, Liang Q (1999) Type-2 fuzzy logic system. IEEE Trans Fuzzy Syst 7(6):643–658CrossRef

Kayacan E, Ramon H, Kaynakand O, Saey W (2015) Towards agrobots: trajectory control of an autonomous tractor using type-2 fuzzy logic controllers. Mechatron IEEE/ASME Trans 20(1):287–298CrossRef

Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Practice Hall, New JereyMATH

Liang Q, Mendel JM (2000) Interval type-2 fuzzy logic systems: theory and design. Fuzzy Syst IEEE Trans 8(5):535–550CrossRef

Lučić P, Teodorović D (2003b) Vehicle routing problem with uncertain demand at nodes: the bee system and fuzzy logic approach. In: Verdegay JL (ed) Fuzzy sets in optimization. Springer, Berlin, pp 67–82

Mamdani EH (1974) Applications of fuzzy algorithms for simple dynamic plant. Proc IEEE 121(12):1585–1588

Martinez R, Castillo O, Aguilar L (2009) Optimization of type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms. Inf Sci 179(13):2158–2174CrossRefMATH

Melendez A, Castillo O (2012) Optimization of type-2 fuzzy reactive controllers for an autonomous mobile robot. NaBIC 2012:207–211

Melin P, Olivas F, Castillo O, Valdez F, Soria J, Valdez M (2013) Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Syst Appl 40(8):1–12

Mendel JM (2001) Uncertain rule-based fuzzy logic system: introduction and new directions. Practice Hall, New JerseyMATH

Mendel JM (2010) A quantitative comparison of interval type-2 and type1 fuzzy logic systems: first results. In: Proceedings of the IEEE international conference on fuzzy systems (FUZZ), pp 1–8

Mendel JM (2013) On KM algorithms for solving type-2 fuzzy set problems. IEEE Trans Fuzzy Syst 21(3):426–446CrossRef

Mendel JM, John RIB (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10(2):117–127CrossRef

Mendel JM, Hagras H, Tan W-W, Melek WW, Ying H (2014) Introduction to type-2 fuzzy logic control. Wiley, HobokenCrossRefMATH

Mendel JM, Rajati MR, Sussner P (2016) On clarifying some definitions and notations used for type-2 fuzzy sets as well as some recommended changes. Inf Sci 340:337–345MathSciNetCrossRef

Mizumoto M, Tanaka K (1976) Some properties of fuzzy sets of Type-2. In: Information and control, pp 312–340

Ochoa P, Castillo O (2014) Differential evolution with dynamic adaptation of parameters of the optimization of fuzzy controller. In: Recent advance of hybrid approach for designing intelligent systems, pp 275–288

Pagola M, Lopez-Molina C, Fernandez J, Barrenechea E, Bustince H (2013) Interval type-2 fuzzy sets constructed from several membership functions: application to the fuzzy thresholding algorithm. IEEE Trans Fuzzy Syst 21(2):230–244CrossRef

Sepúlveda R, Rodriguez A, Castillo O, Sepúlveda R (2007) Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic. Inf Sci 177(10):2023–22048CrossRef

Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In: Evolutionary computation, 2001. Proceedings of the 2001 congress on, vol 1, pp 101–106. IEEE

Siliciano B, Sciavicco L, Villani L, Oriolo G (2010) Robotics: modelling, planning and control, pp 415–418

Teodorović D (2003) Transport modeling by multi-agent systems: a swarm intelligence approach. Transp Plan Technol 26(4):289–312CrossRef

Teodorović D (2008) Swarm intelligence systems for transportation engineering: principles and applications. Transp Res Part C Emerg Technol 16(6):651–782CrossRef

Tiacharoen S, Chatchanayuenyong T (2012) Design and development of an intelligent control by using bee colony optimization technique. Am J Appl Sci 9(9):1464–1471CrossRef

Tong S, Zhang Q (2008) Robust stabilization of nonlinear time-delay interconnected systems via decentralized fuzzy control. Int J Innov Comput Inf Control 4(7):1567–1582

Wang T, Tong S, Li Y (2009) Robust adaptive fuzzy control for nonlinear system with dynamic uncertainties based on backstepping. Int J Innov Comput Inf Control 5(9):2675–2688

Wong LP, Chong ChS (2009) An efficient bee colony optimization algorithm for traveling salesman problem using frequency-based pruning. In: 7th international conference on industrial informatics (INDIN 2009), pp 775–782

Wong LP, Hean Low MY, Chong ChS (2008) Bee colony optimization with local search for traveling salesman problem. In: Proceedings of Second Asia International Conference on Modelling and Simulation, pp 818–823

Wu D (2013) Approaches for reducing the computational cost of interval type-2 fuzzy logic systems: overview and comparisons. IEEE Trans Fuzzy Syst 21(1):80–99CrossRef

Ying H (2000) Fuzzy control and modeling: analytical foundations and applications. IEEE Press, PiscatawayCrossRef

Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353CrossRefMATH

Zadeh LA (1975a) The concept of a lingüistic variable and its application to approximate reasoning. Part I. Inf Sci 8:199–249CrossRefMATH

Zadeh LA (1975b) The concept of a lingüistic variable and its application to approximate reasoning. Part II. Inf Sci 8:301–357CrossRefMATH

Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, fuzzy sets and systems, vol 90. Elsevier, AmsterdamMATH

Zhou H, Ying H (2013) A method for deriving the analytical structure of a broad class of typical interval type-2 Mamdani fuzzy controllers. IEEE Trans Fuzzy Syst 21(3):447–458CrossRef

Title: A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers
Authors: Leticia Amador-Angulo
Oscar Castillo
Publication date: 22-09-2016
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 2/2018
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2354-0

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