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Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic

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Abstract

In this paper, we propose a new method for dynamic parameter adaptation in particle swarm optimization (PSO). PSO is an optimization method inspired in social behavior, which has been applied to different optimization problems obtaining good results. In this paper, we propose an improvement to the convergence and diversity of the swarm in PSO using interval type-2 fuzzy logic. Simulation results show that the proposed approach improves the performance of PSO. A comparison of the proposed method using type-2 fuzzy logic with the original PSO approach, and with PSO using type-1 fuzzy logic for dynamic parameter adaptation is presented.

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References

  • Al-Jaafreh MO, Al-Jumaily AA (2007) Training type-2 fuzzy system by particle swarm optimization. In: IEEE congress on evolutionary computation 2007, CEC 2007. ISBN: 978-1-4244-1339-3

  • Biglarbegian M, Mendel J (2011) On simplifying interval type-2 fuzzy logic systems. In: IEEE transactions on systems, man and cybernetics

  • Castillo O, Melin P (2012) A review on the design and optimization of interval type-2 fuzzy controllers. Appl Soft Comput 12(4):1267–1278

  • Chandra Shill P, Faijul Amin Md, Akhand MAH, Murase K (2012) Optimization of interval type-2 fuzzy logic controller using quantum genetic algorithms. IEEE World Congr Comput Intell, pp 1027–1034

  • Chunshien L, Tsunghan W (2011) Adaptive fuzzy approach to function approximation with PSO and RLSE. Elsevier Exp Syst Appl 38:13266–13273

  • Engelbrecht A (2005) Fundamentals of computational swarm intelligence. University of Pretoria, South Africa

  • Haupt R, Haupt S (2004) Practical genetic algorithms, 2nd edn. A Wiley-Interscience publication, New York

  • Hongbo L, Ajith A (2007) A fuzzy adaptive turbulent particle swarm optimization. Int J Innov Comput Appl 1(1):39–47

    Article  Google Scholar 

  • Jang J, Sun C, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Upper Saddle River

    Google Scholar 

  • Karnik NN, Mendel JM (2001) Centroid of a type-2 fuzzy set. Inf Sci 132:195–220

  • Kennedy J, Eberhart R (2001) Swarm intelligence. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol IV. IEEE Service Center, Piscataway, pp 1942–1948

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

  • Marcin M, Smutnicki C (2005) Test functions for optimization needs. http://www.bioinformaticslaboratory.nl/twikidata/pub/Education/NBICResearchSchool/Optimization/VanKampen/BackgroundInformation/TestFunctions-Optimization.pdf

  • Martinez R, Rodriguez A, Castillo O, Aguilar LT (2010) UABC, Tijuana, Mexico. Type-2 fuzzy logic controllers optimization using genetic algoritms and particle swarm optimization. 2010 IEEE international conference on granular computing (GrC). ISBN: 978-1-4244-7964-1

  • Melin P, Olivas F, Castillo O, Valdez F, Soria J, Garcia J (2013) Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Elsevier Exp Syst Appl 40(8):3196–3206

    Article  Google Scholar 

  • Mendel Jerry M (2001) Uncertain rule-based fuzzy logic systems: introduction and new directions. Prentice Hall PTR, Upper Saddle River

    Google Scholar 

  • Mendel J, John R (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10(2):117–127

    Article  Google Scholar 

  • Muthukaruppan S, Er M (2012) A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Elsevier Exp Syst Appl 39:11657–11665

    Article  Google Scholar 

  • Oha Sung-Kwun, Janga Han-Jong, Pedrycz Witold (2011) A comparative experimental study of type-1/type-2 fuzzy cascade controller based on genetic algorithms and particle swarm optimization. Exp Syst Appl 38(9):11217–11229

    Article  Google Scholar 

  • Sepulveda R, Castillo O, Melin P, Rodriguez-Diaz A, Montiel O (2007) Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic. Inf Sci 177(10):2023–2048

    Article  Google Scholar 

  • Shi Y, Eberhart R (2001) Fuzzy Adaptive Particle Swarm Optimization. In: Proceeding of IEEE International conference on evolutionary computation, Piscataway, NJ: IEEE Service Center, Seoul, Korea, pp 101–106

  • Taher N, Ehsan A, Masoud J (2012) A new hybrid evolutionary algorithm based on new fuzzy adaptive PSO and NM algorithms for distribution feeder reconfiguration. Elsevier Energy Convers Manag 54:7–16

    Article  Google Scholar 

  • Wang B, Liang G, ChanLin W, Yunlong D (2006) A new kind of fuzzy particle swarm optimization fuzzy\_PSO algorithm. In: 1st international symposium on systems and control in aerospace and astronautics, ISSCAA 2006, pp 309–311

  • Wu D (2010) A brief tutorial on interval type-2 fuzzy sets and systems (July 22, 2010)

  • Wu D (2011) An interval type-2 fuzzy logic system cannot be implemented by traditional type-1 fuzzy logic systems. In: World conference on soft computing, San Francisco, CA, May 2011, submitted

  • Wu D (2011) Comparison and practical implementation of type-reduction algorithms for type-2 fuzzy sets and systems. In: World conference on soft computing, San Francisco, May 2011

  • Wu D (2011) P-map an intuitive plot to visualize, understand, and compare variable-gain PI controllers, AIS 2011, pp 189–198

  • Wu D (2012) A constrained representation theorem for interval type-2 fuzzy sets using convex and normal embedded type-1 fuzzy sets, and its application to centroid computation

  • Wu D, Mendel JM (2011) On the continuity of type-1 and interval type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 19(1):179–192

  • Wu D, Tan WW (2010) Proceedings of interval type-2 fuzzy PI controllers why they are more robust. In: IEEE international conference on granular computing, San Jose

  • Zadeh L (1965a) Fuzzy sets. Inf Control 8:338–353

  • Zadeh L (1965b) Fuzzy logic. IEEE Comput 8:83–92

  • Zadeh L (1975) The concept of a linguistic variable and its application to approximate reasoning—I. Inform Sci 8:199–249

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Frumen Olivas, Fevrier Valdez, Oscar Castillo & Patricia Melin

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  1. Frumen Olivas
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  2. Fevrier Valdez
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  3. Oscar Castillo
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  4. Patricia Melin
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Corresponding author

Correspondence to Oscar Castillo.

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Communicated by V. Loia.

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Olivas, F., Valdez, F., Castillo, O. et al. Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic. Soft Comput 20, 1057–1070 (2016). https://doi.org/10.1007/s00500-014-1567-3

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  • DOI: https://doi.org/10.1007/s00500-014-1567-3

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