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Parameter Adaptation in the Imperialist Competitive Algorithm Using Generalized Type-2 Fuzzy Logic

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Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 862))

Abstract

In this paper we propose the use of generalized type-2 fuzzy systems to dynamic adjustment the parameters of the imperialist competitive algorithm (ICA), and we take a type-1 fuzzy system as a basis to extend our proposal using generalized type-2 fuzzy logic. The ICA algorithm is based on the concept of imperialism in which the strongest countries try to take control of the weakest countries. In order to measure the performance of our proposed method different benchmark functions were used and finally, a comparison was made between the variants to observe their behavior applied to benchmark mathematical functions.

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References

  1. Bernal, E., Castillo, O., Soria, J.: Fuzzy logic for dynamic adaptation in the imperialist competitive algorithm. In: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7. IEEE (2017)

    Google Scholar 

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

    Article  Google Scholar 

  3. Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence (1997)

    Google Scholar 

  4. Bernal, E., Castillo, O., Soria, J., Valdez, F.: A variant to the dynamic adaptation of parameters in galactic swarm optimization using a fuzzy logic augmentation. In: Proceedings of the 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–7 (2018)

    Google Scholar 

  5. Mahmoodabadi, M.J., Jahanshahi, H.: Multi-objective optimized fuzzy-PID controllers for fourth order nonlinear systems. Eng. Sci. Technol. Int. J. 18, 1084–1098 (2016)

    Article  Google Scholar 

  6. Milajić, A., Beljaković, D., Davidović, N., Vatin, N., Murgul, V.: Using the big bang-big crunch algorithm for rational design of an energy-plus building. Procedia Eng. 117, 916–923 (2015)

    Article  Google Scholar 

  7. Sanchez, M.A., Castillo, O., Castro, J.R., Melin, P.: Fuzzy granular gravitational clustering algorithm for multivariate data. Inf. Sci. 279, 498–511 (2014)

    Article  MathSciNet  Google Scholar 

  8. Sanchez, M.A., Castillo, O., Castro, J.R.: Generalized type-2 fuzzy systems for controlling a mobile robot and a performance comparison with interval type-2 and type-1 fuzzy systems. Expert. Syst. Appl. 42(14), 5904–5914 (2015)

    Article  Google Scholar 

  9. Castillo, O., Amador-Angulo, L., Castro, J.R., Garcia-Valdez, M.: A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems. Inf. Sci. 354, 257–274 (2016)

    Article  Google Scholar 

  10. Bernal, E., Castillo, O., Soria, J.: A fuzzy logic approach for dynamic adaptation of parameters in galactic swarm optimization. In: Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1–6, IEEE (2017)

    Google Scholar 

  11. Cheng, M.Y., Prayogo, D.: Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98–112 (2014)

    Article  Google Scholar 

  12. Engelbrecht, A.P.: Computational Intelligence. Wiley, Pretoria, South Africa (2007)

    Book  Google Scholar 

  13. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  14. Atashpaz-Gargari, E., Hashemzadeh, F., Rajabioun, R., Lucas, C.: C. Colonial competitive algorithm: a novel approach for PID controller design in MIMO distillation column process. Int. J. Intell. Comput. Cybern. 1, 337–355 (2008)

    Article  MathSciNet  Google Scholar 

  15. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an al-gorithm for optimization inspired by imperialistic com-petition. Evol. comput. 4661–4667 (2007)

    Google Scholar 

  16. Bernal, E., Castillo, O. Soria, J.: Imperialist competitive algorithm applied to the optimization of mathematical functions: a parameter variation study. In: Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization, vol. 601, pp. 219–232, Springer International Publishing, Berlin (2015)

    Chapter  Google Scholar 

  17. Bernal, E., Castillo, O., Soria, J.: Imperialist competitive algorithm with dynamic parameter adaptation applied to the optimization of mathematical functions. In: Nature-Inspired Design of Hybrid Intelligent Systems, vol. 667, pp. 329–341. Springer International Publishing, Berlin (2017)

    Google Scholar 

  18. Bernal, E., Castillo, O., Soria, J., Valdez, F.: Imperialist competitive algorithm with dynamic parameter adaptation using fuzzy logic applied to the optimization of mathematical functions. Algorithms 10(1), 18 (2017)

    Article  MathSciNet  Google Scholar 

  19. Haunpt, R.L., Haunpt, S.E.: Practical Genetic Algorithms, 2nd edn. Wiley, Hoboken, NJ, USA (2004)

    Google Scholar 

  20. Hedar, A.R.: Test functions for unconstrained global optimization. [Online] Egypt, Assiut University, Available http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO.htm

  21. Muthiah-Nakarajan, V., Noel, M.M.: Galactic swarm optimization: a new global optimization metaheuristic inspired by galactic motion. Appl. Soft Comput. 38, 771–787 (2016)

    Article  Google Scholar 

  22. Oftadeh, R., Mahjoob, M.J., Shariatpanahi, M.: A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput. Math Appl. 60(7), 2087–2098 (2010)

    Article  Google Scholar 

  23. Sedighizadeh, M., Bakhtiary, R.: Optimal multi-objective reconfiguration and capacitor placement of distribution systems with the hybrid big bang-big crunch algorithm in the fuzzy framework. Ain. Shams. Eng. J. 7, 113–129 (2016)

    Article  Google Scholar 

  24. Valdez, F., Melin, P., Castillo, O.: An improved evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms. Appl. Soft. Comput. 11(2), 2625–2632 (2011)

    Article  Google Scholar 

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Acknowledgements

We would like to express our gratitude to the CONACYT and Tijuana Institute of Technology for the facilities and resources granted for the development of this research.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Emer Bernal, Oscar Castillo, José Soria & Fevrier Valdez

Authors
  1. Emer Bernal
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  2. Oscar Castillo
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  3. José Soria
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  4. Fevrier Valdez
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Corresponding author

Correspondence to Oscar Castillo .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Bernal, E., Castillo, O., Soria, J., Valdez, F. (2020). Parameter Adaptation in the Imperialist Competitive Algorithm Using Generalized Type-2 Fuzzy Logic. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_1

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