Abstract
The ant lion optimizer (ALO) is a novel swarm intelligence optimization algorithm, but its population diversity and convergence precision can be limited in some applications. In this paper, we proposed an approach based on ALO and differential mutation operator that called ALO-DM. In this method, differential mutation operator and greedy strategy enhance the diversity of the population. In addition, combining it with data mining algorithms can be useful and practical in big data analytics problems. The simulation results not only show that the ALO-DM is able to obtain accurate solution, but also demonstrate that it is feasible and effective.
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References
Kennedy, J., Eberhart, R.C., Yuhui, S., Shi, Y.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)
Cheng, S., Shi, Y., Qin, Q., Bai, R.: Swarm intelligence in big data analytics. In: Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., Yao, X. (eds.) IDEAL 2013. LNCS, vol. 8206, pp. 417–426. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41278-3_51
Holland, J.H.: Adaptation in Natural and Artificial System: An Introduction with Application to Biology, Control and Artificial Intelligence. University of Michigan Press, Ann Arbor (1975)
Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceeding of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)
Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the First European Conference on Artificial Life, pp. 134–142. Elsevier Publishing, Paris (1991)
Yang, X.S., Deb, S.: Cuckoo search via Levy flights. In: Proceedings of the World Congress on Nature and Biologically Inspired Computing, NaBIC 2009, pp. 210–214. IEEE Publication, USA (2009)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for search. Technical report SFI-TR-95-02-010. Santa Fe Institute (1995)
Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)
Yao, P., Wang, H.: Dynamic Adaptive Ant Lion Optimizer applied to route planning for unmanned aerial vehicle. Soft. Comput. 21(18), 5475–5488 (2016)
Zawbaa, H.M., Emary, E., Grosan, C.: Feature selection via chaotic antlion optimization. PLoS ONE 11(3), e0150652 (2016)
Emary, E., Zawbaa, H.M., Hassanien, A.E.: Binary ant lion approaches for feature selection. Neurocomputing 213, 54–65 (2016)
Rajan, A., Jeevan, K., Malakar, T.: Weighted elitism based Ant Lion Optimizer to solve optimum VAr planning problem. Appl. Soft Comput. 55, 352–370 (2017)
Scharf, I., Ovadia, O.: Factors influencing site abandonment and site selection in a sit-and-wait predator: a review of pit-building antlion larvae. J. Insect Behav. 19, 197–218 (2006)
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. Test functions for optimization needs (2005)
Yang, X.S.: Test problems in optimization. arXiv preprint arXiv:1008.0549 (2010)
Acknowledgments
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. This work is supported in part by the National Natural Science Foundation of China under Grant 61170035, 61272420 and 81674099, Six talent peaks project in Jiangsu Province (Grant No. 2014 WLW-004), the Fundamental Research Funds for the Central Universities (Grant No. 30916011328).
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Hu, P. et al. (2018). ALO-DM: A Smart Approach Based on Ant Lion Optimizer with Differential Mutation Operator in Big Data Analytics. In: Liu, C., Zou, L., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10829. Springer, Cham. https://doi.org/10.1007/978-3-319-91455-8_6
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DOI: https://doi.org/10.1007/978-3-319-91455-8_6
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