Abstract
Metaheuristics have been proposed as an alternative to mathematical optimization methods to address non convex problems involving large search spaces. Within this context a new promising metaheuristic inspired from earth atmosphere phenomena and termed as Wind Driven Optimization (WDO) has been developed by Bayraktar. WDO has been successfully applied to solve continuous optimization problems. However it requires tuning several parameters and it may lead to premature convergence. In this paper the basic WDO is modified in a way to improve the search capabilities of the algorithm and to reduce the number of tunable parameters. In the proposed variant of WDO, the original model equation is modified by introducing a pressure based term to replace the rank based term. Furthermore, the value of the gravitational term is automatically and adaptively set. The performance of the proposed modified WDO has been assessed using several benchmarks in numerical optimization. The obtained results show that the modified WDO outperforms the original WDO in most test problems from both accuracy and robustness.
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
Chong, E., Zak, S.: An Introduction to Optimization, 3rd edn. Wiley, Hoboken (2008)
Xing, L., Chen, Y., Cai, H.: An intelligent genetic algorithm designed for global optimization of multi-minima functions. Appl. Math. Comput. 178, 355–371 (2006)
Tsoulos, I.G.: Modifications of real code genetic algorithm for global optimization. Appl. Math. Comput. 203, 598–607 (2008)
Kennedy, J.: Stereotyping: improving particle swarm performance with cluster analysis. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 1507–1512 (2000)
Dorigoand, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Leguizamon, G., Blum, C., Alba, E.: Evolutionary computation. In: Gonzalez, T. (ed.) Handbook of Approximation Algorithms and Metaheuristics, pp. 24.1–24.X. CRC Press, Boca Raton (2007)
Bayraktar, Z., Komurcu, M., Werner, D.H.: Wind driven optimization (WDO): a novel nature-inspired optimization algorithm and its application in electromagnetics. In: IEEE International Symposium on Antennas and Propagation Society, pp. 1–4 (2010)
Bayraktar, Z., Komurcu, M., Bossard, J.A., Werner, D.H.: The wind driven optimization technique and its application in electromagnetics. IEEE Trans. Antennas Propag. 61(5), 2745–2757 (2013)
Boulesnane, A., Meshoul, S.: A new multi-region modified wind driven optimization algorithm with collision avoidance for dynamic environments. In: Tan, Y., Shi, Y., Coello, C.A. (eds.) ICSI 2014, Part II. LNCS, vol. 8795, pp. 412–421. Springer, Heidelberg (2014)
Chao, C.W., Fang, S.C., Liao, C.J.: A Tropical Cyclone-Based Method For Global Optimization. J. Ind. Manage. Optim. 8, 103–115 (2012)
Williamson, D.F., Parker, R.A., Kendrick, J.S.: The box plot: a simple visual method to interpret data. Ann. Intern. Med. 110(11), 916 (1989)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Boulesnane, A., Meshoul, S. (2015). A Modified Wind Driven Optimization Model for Global Continuous Optimization. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-19644-2_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19643-5
Online ISBN: 978-3-319-19644-2
eBook Packages: Computer ScienceComputer Science (R0)