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Firefly Algorithm and Grey Wolf Optimizer for Constrained Real-Parameter Optimization

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Fuzzy Techniques: Theory and Applications (IFSA/NAFIPS 2019 2019)

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Abstract

The main goal of this paper is to present the performance of two popular algorithms, the first is the Firefly Algorithm (FA) and the second one is the Grey Wolf Optimizer (GWO) algorithm for complex problems. In this case the problems that we are presenting are of the CEC 2017 Competition on Constrained Real-Parameter Optimization in order to realize a brief analysis, study and comparison between the FA and GWO algorithms respectively.

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References

  1. Maier, H.R., Kapelan, Z.: Evolutionary algorithms and other metaheuritics in water resources: current status, research challenges and future directions. Environ. Model. Softw. 62, 271–299 (2014)

    Article  Google Scholar 

  2. Can, U., Alatas, B.: Physics based metaheuristic algorithms for global optimization. Am. J. Inf. Sci. Comput. Eng. 1, 94–106 (2015)

    Google Scholar 

  3. Yang, X., Karamanoglu, M.: Swarm intelligence and bio-inspired computation: an overview. In: Swarm Intelligence and Bio-Inspired Computation, pp. 3–23 (2013)

    Chapter  Google Scholar 

  4. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)

    Article  Google Scholar 

  5. Yang, X-S.: Firefly Algorithm, Lévy Flights and Global Optimization arXiv:1003.1464v1 (2010)

  6. Yang, X.-S.: Firefly Algorithm: Recent Advances and Applications arXiv:1308.3898v1 (2013)

    Article  Google Scholar 

  7. Mirjalili, S., Mirjalili, M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  8. Muro, C., Escobedo, R., Spector, L., Coppinger, R.: Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav. Process. 88, 192–197 (2011)

    Article  Google Scholar 

  9. Rodríguez, L., Castillo, O., Valdez, M., Soria, J.: A comparative study of dynamic adaptation of parameters in the GWO algorithm using type-1 and interval type-2 fuzzy logic. In: Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, pp. 3–17 (2018)

    Google Scholar 

  10. Digalakis, J., Margaritis, K.: On benchmarking functions for genetic algorithms. Int. J. Comput. Math. 77, 481–506 (2001)

    Article  MathSciNet  Google Scholar 

  11. Molga, M., Smutnicki, C.: Test functions for optimization needs. Test functions for optimization needs (2005)

    Google Scholar 

  12. Yang, X.-S.: Test problems in optimization, arXiv, preprint arXiv:1008.0549 (2010)

  13. Guohua, W., Mallipeddi, R., Suganthan, P.N.: Problem Definitions and Evaluation Criteria for the CEC 2017 Competition on Constrained Real-Parameter Optimization (2017)

    Google Scholar 

  14. Lagunes, M., Castillo, O., Soria, J.: Optimization of membership functions parameters for fuzzy controller of an autonomous mobile robot using the firefly algorithm. In: Fuzzy Logic Augmentation of Neural and Optimization Algorithms, pp. 199–206 (2018)

    Google Scholar 

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

    Article  Google Scholar 

  16. Larson, R., Farber, B.: Elementary Statistics Picturing the World, pp. 428–433. Pearson Education Inc. (2003)

    Google Scholar 

  17. Gonzalez, B., Melin, P., Valdez, F., Prado-Arechiga, G.: 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, pp. 17–27 (2018)

    Google Scholar 

  18. 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, pp. 329–341 (2017)

    Google Scholar 

  19. Barraza, J., Melin, P., Valdez, F., Gonzalez, C.I.: Fuzzy fireworks algorithm based on a sparks dispersion measure. Algorithms 10, 83 (2017)

    Article  Google Scholar 

  20. Barraza, J., Melin, P., Valdez, F., Gonzalez, C.: Fuzzy FWA with dynamic adaptation of parameters. In: IEEE CEC 2016, pp. 4053–4060 (2016)

    Google Scholar 

  21. Rodríguez, L., Castillo, O., García, M., Soria, J.: A comparative study of dynamic adaptation of parameters in the GWO algorithm using type-1 and interval type-2 fuzzy logic. In: Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, pp. 3–16 (2018)

    Google Scholar 

  22. Caraveo, C., Valdez, F., Castillo, O.: Optimization mathematical functions for multiple variables using the algorithm of self-defense of the plants. In: Nature-Inspired Design of Hybrid Intelligent Systems, pp. 631–640 (2017)

    Google Scholar 

  23. Guerrero, M., Castillo, O., Garcia, M.: Cuckoo search algorithm via lévy flight with dynamic adaptation of parameter using fuzzy logic for benchmark mathematical functions. In: Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, pp. 555–571 (2016)

    Chapter  Google Scholar 

  24. Leal-Ramírez, C., Castillo, O., Melin, P., Rodríguez-Díaz, A.: 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 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  29. Melin, P., González, C.I., Castro, J.R., Mendoza, O., Castillo, O.: Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22(6), 1515–1525 (2014)

    Article  Google Scholar 

  30. González, C.I., Melin, P., Castro, J.R.: Oscar castillo, olivia mendoza: optimization of interval type-2 fuzzy systems for image edge detection. Appl. Soft Comput. 47, 631–643 (2016)

    Article  Google Scholar 

  31. González, C.I., Melin, P., Castro, J.R.: Olivia mendoza, oscar castillo: an improved sobel edge detection method based on generalized type-2 fuzzy logic. Soft. Comput. 20(2), 773–784 (2016)

    Article  Google Scholar 

  32. Ontiveros, E., Melin, P., Castillo, O.: High order α-planes integration: a new approach to computational cost reduction of general type-2 fuzzy systems. Eng. Appl. AI 74, 186–197 (2018)

    Google Scholar 

  33. Melin, P., Castillo, O.: Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach. IEEE Trans. Ind. Electron. 48(5), 951–955 (2001)

    Article  Google Scholar 

  34. Aguilar, L., Melin, P., Castillo, O.: Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy ANFIS approach. Appl. Soft Comput. 3(3), 209–219 (2003)

    Article  Google Scholar 

  35. Melin, P., Castillo, O.: Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory. Appl. Soft Comput. 3(4), 353–362 (2003)

    Article  Google Scholar 

  36. Melin, P., Amezcua, J., Valdez, F., Castillo, O.: A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias. Inf. Sci. 279, 483–497 (2014)

    Article  MathSciNet  Google Scholar 

  37. Melin, P., Castillo, O.: Modelling, Simulation and Control of Non-Linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory. CRC Press, Boca Raton (2001)

    Book  Google Scholar 

  38. Melin, P., Sánchez, D., Castillo, O.: Genetic optimization of modular neural networks with fuzzy response integration for human recognition. Inf. Sci. 197, 1–19 (2012)

    Article  Google Scholar 

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Correspondence to Oscar Castillo .

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Rodríguez, L., Castillo, O., García, M., Soria, J. (2019). Firefly Algorithm and Grey Wolf Optimizer for Constrained Real-Parameter Optimization. In: Kearfott, R., Batyrshin, I., Reformat, M., Ceberio, M., Kreinovich, V. (eds) Fuzzy Techniques: Theory and Applications. IFSA/NAFIPS 2019 2019. Advances in Intelligent Systems and Computing, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-21920-8_47

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