Abstract
Designing and modeling an optimization algorithm with dedicated search is a costly process and it need a deep analysis of problem. In this regard, heuristic and hybrid of heuristic algorithms have been widely used to solve optimization problems because they have been provided efficient way to find an approximate solution but they are limited to use number of different heuristic algorithm and they are so problem-depend. Hyper-heuristic is a set of heuristics, meta- heuristics, and high-level search strategies that work on the heuristic search space instead of solution search space. Hyper-heuristics techniques have been employed to develop approaches that are more general than optimization search methods and traditional techniques. The aim of a hyperheuristic algorithms is to reduce the amount of domain knowledge by using the capabilities of high-level heuristics and the abilities of low-level heuristics simultaneously in the search strategies. In this study, an efficient robust hyperheuristic clustering algorithm is proposed to find the robust and optimum clustering results based on a set of easy-to-implement low-level heuristics. Several data sets are tested to appraise the performance of the suggested approach. Reported results illustrate that the suggested approach can provide acceptable results than the alternative methods.
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References
Gan, G., Ma, C., Wu, J.: Data clustering: theory, algorithms, and applications, vol. 20. Siam (2007)
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3), 459–471 (2007)
Boushaki, S.I., Kamel, N., Bendjeghaba, O.: A new quantum chaotic cuckoo search algorithm for data clustering. Expert Syst. Appl. 96, 358–372 (2018)
Li, H., et al.: An improved pigeon-inspired optimization for clustering analysis problems. Int. J. Comput. Intell. Appl. 16(02), 1750014 (2017)
Sun, L., et al.: An Optimized Clustering Method with Improved Cluster Center for Social Network Based on Gravitational Search Algorithm. Springer, Cham (2017)
Shatnawi, N.M.: Data clustering using Lévy flight and local memory bees algorithm. Int. J. Bus. Intell. Data Min. 12(1), 14–24 (2017)
Babrdelbonb, M., Hashim, S.Z.M.H.M., Bazin, N.E.N.: Data analysis by combining the modified k-means and imperialist competitive algorithm. Jurnal Teknologi 70(5) (2014)
Bonab, M.B., Hashim, S.Z.M.: Image segmentation with genetic clustering using weighted combination of particle swarm optimization. In: 14th International Conference on Applied Computer and Applied Computational Science (ACACOS 2015) (2015)
Bonab, M., et al.: Modified K-means combined with artificial bee colony algorithm and differential evolution for color image segmentation. In: Phon-Amnuaisuk, S., Au, T.W. (eds.) Computational Intelligence in Information Systems, pp. 221–231. Springer, Cham(2015)
Bonab, M.B., Mohd Hashim, S.Z.: Improved k-means clustering with Harmonic-Bee algorithms. In: Fourth World Congress on Information and Communication Technologies (WICT) (2014)
Kao, Y.-T., Zahara, E., Kao, I.W.: A hybridized approach to data clustering. Expert Syst. Appl. 34(3), 1754–1762 (2008)
Mısır, M., et al.: An Intelligent Hyper-Heuristic Framework for CHeSC 2011. In: Hamadi, Y., Schoenauer, M. (eds.) Learning and Intelligent Optimization, pp. 461–466. Springer, Heidelberg (2012)
Mısır, M., et al.: A new hyper-heuristic as a general problem solver: an implementation in HyFlex. J. Sched. 16(3), 291–311 (2013)
Niknam, T., Amiri, B.: An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Appl. Soft Comput. 10(1), 183–197 (2010)
Acknowledgment
The authors would like to express their cordial thanks to Universiti Tunku Abdul Rahman (UTAR) for research university grant with number of (4461/002).
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Bonab, M.B., Tay, Y.H., Mohd Hashim, S.Z., Soon, K.T. (2019). An Efficient Robust Hyper-Heuristic Algorithm to Clustering Problem. In: Omar, S., Haji Suhaili, W., Phon-Amnuaisuk, S. (eds) Computational Intelligence in Information Systems. CIIS 2018. Advances in Intelligent Systems and Computing, vol 888. Springer, Cham. https://doi.org/10.1007/978-3-030-03302-6_5
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DOI: https://doi.org/10.1007/978-3-030-03302-6_5
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