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Harmony Search with Dynamic Adaptation of Parameters for the Optimization of a Benchmark Set of Functions

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

Part of the book series: Studies in Computational Intelligence ((SCI,volume 827))

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Abstract

In this paper a fuzzy search algorithm harmony (FHS) is presented. The main difference between previous work is that this method uses a fuzzy system for dynamic parameter adaptation of the two main parameters throughout the iterations of the algorithm, which are: harmony memory accepting (HMR) and pitch adjustment (PArate), with the rules of the fuzzy system control the intensification and diversification of the search space is achieved. This method was applied to the mathematical functions provided by the CEC 2017, which are unimodal, multimodal, hybrid and composite functions to verify the efficiency of the proposed method. A comparison is presented to verify the results obtained with the original harmony search algorithm and the fuzzy harmony search algorithm.

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References

  1. F. Olivas, F. Valdez, O. Castillo, P. Melin, Theory and background, in Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic (pp. 3–10). (Springer International Publishing, Cham, 2018)

    Chapter  Google Scholar 

  2. L. Amador-Angulo, O. Castillo, 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), 571–594 (2018)

    Article  Google Scholar 

  3. C. Caraveo, F. Valdez, O. Castillo, A new optimization meta-heuristic algorithm based on self-defense mechanism of the plants with three reproduction operators. Soft Comput. (Apr. 2018)

    Google Scholar 

  4. C.-M. Wang, Y.-F. Huang, Self-adaptive harmony search algorithm for optimization. Expert Syst. Appl. 37(4), 2826–2837 (2010)

    Article  Google Scholar 

  5. K.S. Lee, Z.W. Geem, A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194(36–38), 3902–3933 (2005)

    Article  Google Scholar 

  6. P. Ochoa, O. Castillo, J. Soria, Interval Type-2 fuzzy logic dynamic mutation and crossover parameter adaptation in a fuzzy differential evolution method, in Intuitionistic Fuzziness and Other Intelligent Theories and Their Applications, vol. 757, ed. by M. Hadjiski, K.T. Atanassov (Springer International Publishing, Cham, 2019), pp. 81–94

    Google Scholar 

  7. D. Zou, L. Gao, Y. Ge, P. Wu, A novel global harmony search algorithm for chemical equation balancing, in 2010 International Conference On Computer Design and Applications, Qinhuangdao, China, pp. V2-1–V2-5 (2010)

    Google Scholar 

  8. R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)

    Google Scholar 

  9. E. Bernal, O. Castillo, J. Soria, F. Valdez, 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 

  10. E. Bernal, O. Castillo, J. Soria, F. Valdez, Galactic swarm optimization with adaptation of parameters using fuzzy logic for the optimization of mathematical functions, in Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, vol. 749, ed. by O. Castillo, P. Melin, J. Kacprzyk (Springer International Publishing, Cham, 2018), pp. 131–140

    Google Scholar 

  11. Z.W. Geem, K.-B. Sim, Parameter-setting-free harmony search algorithm. Appl. Math. Comput. 217(8), 3881–3889 (2010)

    MathSciNet  MATH  Google Scholar 

  12. P. Ochoa, O. Castillo, J. Soria, Differential evolution algorithm using a dynamic crossover parameter with fuzzy logic applied for the CEC 2015 benchmark functions, in Fuzzy Information Processing, vol. 831, ed. by G.A. Barreto, R. Coelho (Springer International Publishing, Cham, 2018), pp. 580–591

    Google Scholar 

  13. O. Castillo, P. Ochoa, J. Soria, Differential evolution with fuzzy logic for dynamic adaptation of parameters in mathematical function optimization, in Imprecision and Uncertainty in Information Representation and Processing, vol. 332, ed. by P. Angelov, S. Sotirov (Springer International Publishing, Cham, 2016), pp. 361–374

    Google Scholar 

  14. M.H. Mashinchi, M.A. Orgun, M. Mashinchi, W. Pedrycz, A tabu-harmony search-based approach to fuzzy linear regression. IEEE Trans. Fuzzy Syst. 19(3), 432–448 (2011)

    Article  Google Scholar 

  15. O. Castillo, C. Soto, F. Valdez, A review of fuzzy and mathematic methods for dynamic parameter adaptation in the firefly algorithm, in Advances in Data Analysis with Computational Intelligence Methods, vol. 738, ed. by A.E. Gawęda, J. Kacprzyk, L. Rutkowski, G.G. Yen (Springer International Publishing, Cham, 2018), pp. 311–321

    Google Scholar 

  16. B. González, P. Melin, F. Valdez, G. Prado-Arechiga, Ensemble neural network optimization using a gravitational search algorithm with interval type-1 and type-2 fuzzy parameter adaptation in pattern recognition applications, in Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, vol. 749, ed. by O. Castillo, P. Melin, J. Kacprzyk (Springer International Publishing, Cham, 2018), pp. 17–27

    Google Scholar 

  17. J. Barraza, L. Rodríguez, O. Castillo, P. Melin, F. Valdez, A new hybridization approach between the fireworks algorithm and grey wolf optimizer algorithm. J. Optim. 2018, 1–18 (2018)

    MathSciNet  MATH  Google Scholar 

  18. M.L. Lagunes, O. Castillo, J. Soria, M. Garcia, F. Valdez, Optimization of granulation for fuzzy controllers of autonomous mobile robots using the firefly algorithm. Granul. Comput. (July 2018)

    Google Scholar 

  19. M. Mahdavi, M. Fesanghary, E. Damangir, An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188(2), 1567–1579 (2007)

    MathSciNet  MATH  Google Scholar 

  20. Y.Y. Moon, Z.W. Geem, G.-T. Han, Vanishing point detection for self-driving car using harmony search algorithm. Swarm Evol. Comput. 41, 111–119 (2018)

    Article  Google Scholar 

  21. Y.-H. Kim, Y. Yoon, Z.W. Geem, A comparison study of harmony search and genetic algorithm for the max-cut problem. Swarm Evol. Comput. (Feb 2018)

    Google Scholar 

  22. Z.W. Geem, S.Y. Chung, J.-H. Kim, Improved optimization for wastewater treatment and reuse system using computational intelligence. Complexity 2018, 1–8 (2018)

    Article  Google Scholar 

  23. A.W. Mohamed, A.A. Hadi, A.M. Fattouh, K.M. Jambi, LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems, in 2017 IEEE Congress on Evolutionary Computation (CEC), Donostia, San Sebastián, Spain, pp. 145–152 (2017)

    Google Scholar 

  24. J. Brest, M. S. Maucec, B. Boskovic, Single objective real-parameter optimization: Algorithm jSO, in 2017 IEEE Congress on Evolutionary Computation (CEC), Donostia, San Sebastián, Spain, pp. 1311–1318 (2017)

    Google Scholar 

  25. D. Manjarres et al., A survey on applications of the harmony search algorithm. Eng. Appl. Artif. Intell. 26(8), 1818–1831 (2013)

    Article  Google Scholar 

  26. Cinthia Peraza, Fevrier Valdez, Patricia Melin, Optimization of intelligent controllers using a type-1 and interval type-2 fuzzy harmony search algorithm. Algorithms 10(3), 82 (2017)

    Article  MathSciNet  Google Scholar 

  27. C. Peraza, F. Valdez, M. Garcia, P. Melin, O. Castillo, A new fuzzy harmony search algorithm using fuzzy logic for dynamic parameter adaptation. Algorithms 9(4), 69 (2016)

    Article  MathSciNet  Google Scholar 

  28. C. Peraza, F. Valdez, J.R. Castro, O. Castillo, Fuzzy dynamic parameter adaptation in the harmony search algorithm for the optimization of the ball and beam controller. Adv. Oper. Res. 2018, 1–16 (2018)

    Article  Google Scholar 

  29. C. Peraza, F. Valdez, O. Castillo, Study on the use of type-1 and interval type-2 fuzzy systems applied to benchmark functions using the fuzzy harmony search algorithm, in Fuzzy logic in intelligent system design, vol. 648, ed. by P. Melin, O. Castillo, J. Kacprzyk, M. Reformat, W. Melek (Springer International Publishing, Cham, 2018), pp. 94–103

    Chapter  Google Scholar 

  30. C. Peraza, F. Valdez, O. Castillo, Improved method based on type-2 fuzzy logic for the adaptive harmony search algorithm, in Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, vol. 749, ed. by O. Castillo, P. Melin, J. Kacprzyk (Springer International Publishing, Cham, 2018), pp. 29–37

    Google Scholar 

  31. C. Leal Ramírez, O. Castillo, P. Melin, A. Rodríguez Díaz, Simulation of the bird age-structured population growth based on an interval type-2 fuzzy cellular structure. Inf. Sci. 181(3), 519–535 (2011)

    Article  MathSciNet  Google Scholar 

  32. N.R. Cázarez-Castro, L.T. Aguilar, O. Castillo, Designing type-1 and type-2 fuzzy logic controllers via fuzzy lyapunov synthesis for nonsmooth mechanical systems. Eng. Appl. of AI 25(5), 971–979 (2012)

    Article  Google Scholar 

  33. O. Castillo, P. Melin, Intelligent systems with interval type-2 fuzzy logic. Int. J. Innov. Comput. Inf. Control 4(4), 771–783 (2008)

    Google Scholar 

  34. G.M. Mendez, O. Castillo, Interval type-2 TSK fuzzy logic systems using hybrid learning algorithm, in The 14th IEEE International Conference on Fuzzy Systems FUZZ’05, 230–235 (2005)

    Google Scholar 

  35. P. Melin, O. Castillo, Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach. IEEE Trans. Ind. Electr. 48(5), 951–955

    Article  Google Scholar 

  36. E. Rubio, O. Castillo, F. Valdez, P. Melin, C.I. González, G. Martinez, An extension of the fuzzy possibilistic clustering algorithm using type-2 fuzzy logic techniques. Adv. Fuzzy Syst., 7094046:1–7094046:23 (2017)

    Article  Google Scholar 

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Acknowledgements

We would like to express our thanks to 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, BC, Mexico

    Cinthia Peraza, Fevrier Valdez & Oscar Castillo

Authors
  1. Cinthia Peraza
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  2. Fevrier Valdez
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  3. Oscar Castillo
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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

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Peraza, C., Valdez, F., Castillo, O. (2020). Harmony Search with Dynamic Adaptation of Parameters for the Optimization of a Benchmark Set of Functions. In: Castillo, O., Melin, P. (eds) Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. Studies in Computational Intelligence, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-030-34135-0_8

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