Abstract
The main goal of this paper is to present a general study of the Grey Wolf Optimizer algorithm. We perform tests to determine in the first part which parameters are candidates to be dynamically adjusted and in the second stage to determine which are the parameters that have the greatest effect in the performance of the algorithm. We also present a justification and results of experiments as well as the benchmark functions that were used for the tests that are presented. In addition we are presenting a simple fuzzy system with the results obtained based on this general study.
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References
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. OUP, Oxford (1999)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, 1995 Proceedings, pp. 1942–1948 (1995)
Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. Comput. Intell. Mag. 1, 28–39 (2006). IEEE
Basturk, B., Karaboga, D.: An artificial bee colony (ABC) algorithm for numeric function optimization. In: IEEE Swarm Intelligence Symposium, pp. 687–697 (2006)
Mirjalili, S., Mirjalili, M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. In: Dario, P., Dario, P., Sandini, G., Aebischer, P. (eds.) Robots and biological systems: towards a new bionics?. NATO ASI Series, pp. 703–712. Springer, Heidelberg (1993)
Maier, H.R., Kapelan, Z.: Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions. Environ. Model Softw. 62, 271–299 (2014)
Can, U., Alatas, B.: Physics based metaheuristic algorithms for global optimization. Am. J. Inf. Sci. Comput. Eng. 1, 94–106 (2015)
Yang, X., Karamanoglu, M: Swarm intelligence and bio-inspired computation: an overview. Swarm Intell. Bio-Inspired Comput. 1, 3–23 (2013)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)
Muro, C., Escobedo, R., Spector, L., Coppinger, R.: Wolfpack (canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav. Process. 88, 192–197 (2011)
Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3, 82–102 (1999)
Digalakis, J., Margaritis, K.: On benchmarking functions for genetic algorithms. Int. J. Comput. Math. 77, 481–506 (2001)
Molga, M., Smutnicki, C.: Test functions for optimization needs (2005)
Yang, X-S.: Test problems in optimization. arXiv, preprint arXiv:1008.0549 (2010)
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Rodríguez, L., Castillo, O., Soria, J. (2017). A Study of Parameter Dynamic Adaptation with Fuzzy Logic for the Grey Wolf Optimizer Algorithm. 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_19
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DOI: https://doi.org/10.1007/978-3-319-62434-1_19
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