Abstract
This paper describes the enhancement of the Water Cycle Algorithm (WCA) using a fuzzy inference system to adapt its parameters dynamically. The original WCA is compared regarding performance with the proposed method called Water Cycle Algorithm with Dynamic Parameter Adaptation (WCA-DPA). Simulation results on a set of well-known test functions show that the WCA can be improved with a fuzzy dynamic adaptation of the parameters.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE Congress on Evolutionary Computation, pp. 4661–4667. IEEE (2007)
Eskandar, H., et al.: Water Cycle Algorithm - A Novel Metaheuristic Optimization Method for Solving Constrained Engineering Optimization Problems. Comput. Struct. 110–111, 151–166 (2012)
Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Goldberg, D.E.: Genetic Algorithms in Search. Optimization and Machine Learning. Addison-Wesley Longman Publishing Co. Inc., Boston (1989)
Hosseini, H.S.: Problem solving by intelligent water drops. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 3226–3231 (2007)
Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans. Syst. Man, Cybern. Part B 35(6), 1272–1282 (2005)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Mach. Stud. 7(1), 1–13 (1975)
Melin, P., et al.: Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Syst. Appl. 40(8), 3196–3206 (2013)
Mirjalili, S., et al.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Sadollah, A., et al.: Water cycle algorithm for solving multi-objective optimization problems. Soft. Comput. 19(9), 2587–2603 (2015)
Sadollah, A., et al.: Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems. Appl. Soft Comput. 30, 58–71 (2015)
Shi, Y., Eberhart, R.C.: Fuzzy adaptive particle swarm optimization. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 101–106 (2001)
Shreve, R.L.: Infinite topologically random channel networks. J. Geol. 75(2), 178–186 (1967)
Shreve, R.L.: Statistical law of stream numbers. J. Geol. 74(1), 17–37 (1966)
Sombra, A., et al.: A new gravitational search algorithm using fuzzy logic to parameter adaptation. In: 2013 IEEE Congress on Evolutionary Computation, pp. 1068–1074 (2013)
Zhan, Z.H., et al.: Adaptive particle swarm optimization. IEEE Trans. Syst. Man Cybern. Part B 39(6), 1362–1381 (2009)
Zheng, Y.-J.: Water wave optimization: {A} new nature-inspired metaheuristic. Comput. {&} {OR} 55, 1–11 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Méndez, E., Castillo, O., Soria, J., Melin, P., Sadollah, A. (2017). Water Cycle Algorithm with Fuzzy Logic for Dynamic Adaptation of Parameters. In: Sidorov, G., Herrera-Alcántara, O. (eds) Advances in Computational Intelligence. MICAI 2016. Lecture Notes in Computer Science(), vol 10061. Springer, Cham. https://doi.org/10.1007/978-3-319-62434-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-62434-1_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62433-4
Online ISBN: 978-3-319-62434-1
eBook Packages: Computer ScienceComputer Science (R0)