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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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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