Abstract
In this paper the main idea is to state that the use of fuzzy logic helps in the improvement of results in different optimization problems. For this particular paper we propose using the methodology of combining fuzzy logic with the differential evolution algorithm to perform experiments with a set of functions of the CEC2015, since these functions are more complicated than traditional benchmark functions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
F. Olivas, F. Valdez, O. Castillo, P. Melin, Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic. Soft. Comput. 20(3), 1057–1070 (2016)
C. Peraza, F. Valdez, O. Castillo, A harmony search algorithm comparison with genetic algorithms, in Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics (Springer International Publishing, Berlin, 2015), pp. 105–123
C. Peraza, F. Valdez, O. Castillo, Fuzzy control of parameters to dynamically adapt the HS algorithm for optimization, in 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) Held Jointly with 2015 5th World Conference on Soft Computing (WConSC) (IEEE, New York, August 2015), pp. 1–6
R. Storn, On the usage of differential evolution for function optimization, in Fuzzy Information Processing Society, 1996. NAFIPS, 1996 Biennial Conference of the North American (IEEE, New York, June 1996), pp. 519–523
R. Storn, K. Price, Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces, vol. 3 (ICSI, Berkeley, 1995)
F. Valdez, P. Melin, O. Castillo, Evolutionary method combining particle swarm optimisation and genetic algorithms using fuzzy logic for parameter adaptation and aggregation: the case neural network optimisation for face recognition. Int. J. Artif. Intel. Soft Comput. 2(1–2), 77–102 (2010)
F. Valdez, P. Melin, O. Castillo, An improved evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms. Appl. Soft Comput. 11(2), 2625–2632 (2011)
O. Castillo, H. Neyoy, J. Soria, P. Melin, F. Valdez, A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot. Appl. Soft Comput. 28, 150–159 (2015)
O. Castillo, P. Melin, Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach. Appl. Soft Comput. 3(4), 363–378 (2003)
R. MartÃnez-Soto, O. Castillo, L.T. Aguilar, Type-1 and Type-2 fuzzy logic controller design using a Hybrid PSO–GA optimization method. Inf. Sci. 285, 35–49 (2014)
P. Melin, O. Castillo, A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition. Appl. Soft Comput. 21, 568–577 (2014)
P. Ochoa, O. Castillo, J. Soria, A fuzzy differential evolution method with dynamic adaptation of parameters for the optimization of fuzzy controllers, in 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW) (IEEE, New York, June 2014), pp. 1–6
A. Al-Dujaili, K. Subramanian, S. Suresh, HumanCog: a cognitive architecture for solving optimization problems, in 2015 IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, May 2015), pp. 3220–3227
N. Awad, M.Z. Ali, R.G. Reynolds, A differential evolution algorithm with success-based parameter adaptation for CEC2015 learning-based optimization, in 2015 IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, May 2015), pp. 1098–1105
D. Aydın, T. Sffltzle, A configurable generalized artificial bee colony algorithm with local search strategies, in 2015 IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, May 2015), pp. 1067–1074
Q. Chen, B. Liu, Q. Zhang, J.J. Liang, P.N. Suganthan, B.Y. Qu, Problem definition and evaluation criteria for CEC 2015 special session and competition on bound constrained single-objective computationally expensive numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, China and Nanyang Technological University, Singapore, Technical report (2014)
S.M. Guo, J.S.H. Tsai, C.C. Yang, P.H. Hsu, 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) (IEEE, New York, May 2015), pp. 1003–1010
R. Poláková, J. TvrdÃk, P. Bujok, Cooperation of optimization algorithms: a simple hierarchical model, in 2015 IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, May 2015), pp. 1046–1052
J.L. Rueda, I. Erlich, Testing MVMO on learning-based real-parameter single objective benchmark optimization problems, in 2015 IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, May 2015), pp. 1025–1032
K.M. Sallam, R.A. Sarker, D.L. Essam, S.M. Elsayed, Neurodynamic differential evolution algorithm and solving CEC2015 competition problems, in 2015 IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, May 2015), pp. 1033–1040
C. Yu, L.C. Kelley, Y. Tan, Dynamic search fireworks algorithm with covariance mutation for solving the CEC 2015 learning based competition problems, in 2015 IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, May 2015), pp. 1106–1112
Y.J. Zheng, X.B. Wu, Tuning maturity model of ecogeography-based optimization on CEC 2015 single-objective optimization test problems, in 2015 IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, May 2015), pp. 1018–1024
K.V. Price, R.M. Storn, J.A. Lampinen, The differential evolution algorithm. Differential Evolution: A Practical Approach to Global Optimization (2005), pp. 37–134
K. Price, R.M. Storn, J.A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization (Springer Science & Business Media, Berlin, 2006)
P. Ochoa, O. Castillo, J. Soria, Differential evolution with dynamic adaptation of parameters for the optimization of fuzzy controllers, in Recent Advances on Hybrid Approaches for Designing Intelligent Systems (Springer International Publishing, Berlin, 2014), pp. 275–288
X. Li, Decomposition and cooperative coevolution techniques for large scale global optimization, in Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation (ACM, New York, July 2014), pp. 819–838
R. Tanabe, A. Fukunaga, Success-history based parameter adaptation for differential evolution, in 2013 IEEE Congress on Evolutionary Computation (IEEE, New York, June 2013), pp. 71–78
L. Chen, C. Peng, H.L. Liu, S. Xie, An improved covariance matrix leaning and searching preference algorithm for solving CEC 2015 benchmark problems, in 2015 IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, May 2015), pp. 1041–1045
C. Leal RamÃrez, O. Castillo, P. Melin, A. RodrÃguez DÃaz, Simulation of the bird age-structured population growth based on an interval type-2 fuzzy cellular structure. Inf. Sci. 181(3), 519–535 (2011)
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. Artif. Intell. 25(5), 971–979 (2012)
O. Castillo, P. Melin, Intelligent systems with interval type-2 fuzzy logic. Int. J. Innovative Comput. Inf. Control 4(4), 771–783 (2008)
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, 2005. FUZZ’05, pp. 230–235
P. Melin, C.I. González, J.R. Castro, O. Mendoza, O. Castillo, Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22(6), 1515–1525 (2014)
C.I. González, P. Melin, J.R. Castro, O. Castillo, O. Mendoza, Optimization of interval type-2 fuzzy systems for image edge detection. Appl. Soft Comput. 47, 631–643 (2016)
C.I. González, P. Melin, J.R. Castro, O. Mendoza, O. Castillo, An improved sobel edge detection method based on generalized type-2 fuzzy logic. Soft. Comput. 20(2), 773–784 (2016)
E. Ontiveros, P. Melin, O. Castillo, High order α-planes integration: a new approach to computational cost reduction of general type-2 fuzzy systems. Eng. Appl. Artif. Intell. 74, 186–197 (2018)
P. Melin, O. Castillo, Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach. IEEE Trans. Ind. Electron. 48(5), 951–955 (2001)
P. Melin, O. Castillo, Modelling, Simulation and Control of Non-linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory (CRC Press, Boca Raton, 2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ochoa, P., Castillo, O., Soria, J. (2020). The Differential Evolution Algorithm with a Fuzzy Logic Approach for Dynamic Parameter Adjustment Using Benchmark 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_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-34135-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34134-3
Online ISBN: 978-3-030-34135-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)