Abstract
Evolutionary algorithm exist as a subclass of artificial intelligence that requires constant optimization. It is an area of immense interest to several researchers. Different biological behaviors has formed the base for implementation of various algorithms like firefly, genetic, bee colony particle swarm optimization. Flower Pollination Algorithm (FPA) is the most recent work in this field, which use the flower pollination technique. Differential Evolution (DE) is a basic and powerful computation showing a strong global optimization. This article gives a brief about FPA and DE. Subsequently, a hybrid algorithm named as ssFPA/DE using the search strategy of flower pollination algorithm in differential evolution is applied in the field of clustering for checking the efficiency of the algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alves, V.S., Campello, R.J., Hruschka, E.R.: Towards a fast evolutionary algorithm for clustering. In: IEEE Congress on Evolutionary Computation, 2006, CEC 2006, pp. 1776–1783. IEEE (2006)
Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Voges, K.E., Pope, N.K.L.: Rough clustering using an evolutionary algorithm. In: 2012 45th Hawaii International Conference on System Science (HICSS), pp. 1138–1145. IEEE (2012)
Jian-Xiang, W., Huai, L., Yue-Hong, S., Xin-Ning, S.: Application of genetic algorithm in document clustering. In: International Conference on Information Technology and Computer Science 2009, ITCS 2009, vol. 1, pp. 145–148. IEEE (2009)
Zhang, Z., Cheng, H., Zhang, S., Chen, W., Fang , Q.: Clustering aggregation based on genetic algorithm for documents clustering. In: IEEE Congress on Evolutionary Computation, CEC 2008, (IEEE World Congress on Computational Intelligence), pp. 3156–3161. IEEE (2008)
Zheng, Z., Gong, M., Ma, J., Jiao, L., Wu, Q.: Unsupervised evolutionary clustering algorithm for mixed type data. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)
Wang, R., Zhou, Y.: Flower pollination algorithm with dimension by dimension improvement. In: Mathematical Problems in Engineering, pp. 1–9 (2014)
MartÃnez-Estudillo, A.C., Hervás-MartÃnez, C., MartÃnez-Estudillo, F.J., GarcÃa-Pedrajas, N.: Hybridization of evolutionary algorithms and local search by means of a clustering method. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 36(3), 534–545 (2005)
Zhou, Y., Wang, R., Luo, Q.: Elite opposition-based flower pollination algorithm. Neurocomputing, 188, 294–310 (2016)
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
Ramadas, M., Abraham, A., Kumar, S. (2017). Using Data Clustering on ssFPA/DE- a Search Strategy Flower Pollination Algorithm with Differential Evolution. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_53
Download citation
DOI: https://doi.org/10.1007/978-3-319-52941-7_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52940-0
Online ISBN: 978-3-319-52941-7
eBook Packages: EngineeringEngineering (R0)