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Differential Evolution Algorithm Using a Dynamic Crossover Parameter with Fuzzy Logic Applied for the CEC 2015 Benchmark Functions

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Book cover Fuzzy Information Processing (NAFIPS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 831))

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Abstract

The study of metaheuristics has become an important area for research, these metaheuristics contain parameters and the literature provides us with a range of values in which the algorithm can have good results. For this paper we propose to use the Differential Evolution algorithm combined with fuzzy logic to enable having dynamic crossover parameter, and to complement this work we include the diversity variable based on Euclidean distance, which will help us to know if the individuals of the population are separated or near in the search space in other words is the exploration and the exploitation in the search space, and for this article we work with two types of Simple Multimodal and Hybrid functions belonging to set of CEC 2015 benchmark functions.

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References

  1. Amador-Angulo, L., Castillo, O.: Statistical analysis of type-1 and interval type-2 fuzzy logic in dynamic parameter adaptation of the BCO. In: IFSA-EUSFLAT, pp. 776–783, June 2015 (2015)

    Google Scholar 

  2. Amador-Angulo, L., Castillo, O.: A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers. Soft Comput. 22(2), 1–24 (2016)

    Google Scholar 

  3. Awad, N., Ali, M.Z., Reynolds, R.G.: A differential evolution algorithm with success-based parameter adaptation for CEC 2015 learning-based optimization. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1098–1105. IEEE, May 2015

    Google Scholar 

  4. 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 

  5. Caraveo, C., Valdez, F., Castillo, O.: Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation. Appl. Soft Comput. 43, 131–142 (2016)

    Article  Google Scholar 

  6. Guo, S.M., Tsai, J.S.H., Yang, C.C., Hsu, P.H.: A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1003–1010. IEEE, May 2015

    Google Scholar 

  7. Martinez, C., Castillo, O., Montiel, O.: Comparison between ant colony and genetic algorithms for fuzzy system optimization. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (Eds.) Soft computing for hybrid intelligent systems, pp. 71–86 (2008)

    Google Scholar 

  8. 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 

  9. Méndez, E., Castillo, O., Soria, J., Sadollah, A.: Fuzzy dynamic adaptation of parameters in the water cycle algorithm. In: Melin, P., Castillo, O., Kacprzyk, J. (eds.) Nature-Inspired Design of Hybrid Intelligent Systems. SCI, vol. 667, pp. 297–311. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47054-2_20

    Chapter  Google Scholar 

  10. Ochoa, P., Castillo, O., Soria, J.: Differential evolution using fuzzy logic and a comparative study with other metaheuristics. In: Melin, P., Castillo, O., Kacprzyk, J. (eds.) Nature-Inspired Design of Hybrid Intelligent Systems. SCI, vol. 667, pp. 257–268. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47054-2_17

    Chapter  Google Scholar 

  11. Peraza, C., Valdez, F., Castillo, O.: An adaptive fuzzy control based on harmony search and its application to optimization. In: Melin, P., Castillo, O., Kacprzyk, J. (eds.) Nature-Inspired Design of Hybrid Intelligent Systems. SCI, vol. 667, pp. 269–283. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47054-2_18

    Chapter  Google Scholar 

  12. Rodríguez, L., Castillo, O., Soria, J., Melin, P., Valdez, F., Gonzalez, C.I., Soto, J.: A fuzzy hierarchical operator in the grey wolf optimizer algorithm. Appl. Soft Comput. 57, 315–328 (2017)

    Article  Google Scholar 

  13. Rueda, J.L., Erlich, I.: Testing MVMO on learning-based real-parameter single objective benchmark optimization problems. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1025–1032. IEEE, May 2015

    Google Scholar 

  14. Sallam, K.M., Sarker, R.A., Essam, D.L., Elsayed, S.M.: Neurodynamic differential evolution algorithm and solving CEC 2015 competition problems. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1033–1040. IEEE, May 2015

    Google Scholar 

  15. Sánchez, D., Melin, P., Castillo, O.: Fuzzy system optimization using a hierarchical genetic algorithm applied to pattern recognition. In: Filev, D., et al. (eds.) Intelligent Systems 2014. AISC, vol. 323, pp. 713–720. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-11310-4_62

    Chapter  Google Scholar 

  16. Solano-Aragón, C., Castillo, O.: Optimization of benchmark mathematical functions using the firefly algorithm with dynamic parameters. In: Castillo, O., Melin, P. (eds.) Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics. SCI, vol. 574, pp. 81–89. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-10960-2_5

    Chapter  Google Scholar 

  17. Valdez, F., Melin, P., Castillo, O.: Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making. In: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2009, August 2009, pp. 2114–2119. IEEE (2009)

    Google Scholar 

  18. Valdez, F., Melin, P., Castillo, O.: An improved evolutionary method with fuzzy logic for combining Particle Swarm Optimization and Genetic Algorithms. Appl. Soft Comput. J. 11(2), 2625–2632 (2011)

    Article  Google Scholar 

  19. Valdez, F., Melin, P., Castillo, O.: Modular Neural Networks architecture optimization with a new nature inspired method using a fuzzy combination of Particle Swarm Optimization and Genetic Algorithms. Inf. Sci. J. 270, 143–153 (2014)

    Article  Google Scholar 

  20. Valdez, F., Melin, P., Castillo, O.: A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation. Expert Syst. Appl. J. 41(14), 6459–6466 (2014)

    Article  Google Scholar 

  21. Valdez, F., Melin, P., Castillo, O.: Toolbox for bio-inspired optimization of mathematical functions. Comp. Applic. Eng. Educ. 22(1), 11–22 (2014)

    Article  Google Scholar 

  22. Valdez, F., Melin, P., Castillo, O.: Comparative study of the use of fuzzy logic in improving particle swarm optimization variants for mathematical functions using co-evolution. Appl. Soft Comput. J. 52, 1070–1083 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Ochoa, Oscar Castillo & José Soria

Authors
  1. Patricia Ochoa
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  2. Oscar Castillo
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  3. José Soria
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Corresponding author

Correspondence to Oscar Castillo .

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Editors and Affiliations

  1. Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Ceará, Brazil

    Guilherme A. Barreto

  2. Department of Statistics & Applied Mathematics, Federal University of Ceará, Fortaleza, Ceará, Brazil

    Ricardo Coelho

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Ochoa, P., Castillo, O., Soria, J. (2018). Differential Evolution Algorithm Using a Dynamic Crossover Parameter with Fuzzy Logic Applied for the CEC 2015 Benchmark Functions. In: Barreto, G., Coelho, R. (eds) Fuzzy Information Processing. NAFIPS 2018. Communications in Computer and Information Science, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-319-95312-0_51

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  • DOI: https://doi.org/10.1007/978-3-319-95312-0_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95311-3

  • Online ISBN: 978-3-319-95312-0

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