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Data skyline query protocol based on parallel genetic improvement decision tree

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Abstract

Query optimization of database requires high speed and high efficiency. In order to solve the low efficiency problem and difficulty in obtaining optimal solution existing in the current query optimization algorithm of database, a query optimization of database based on multi-group firefly algorithm (MGFA) is proposed, combining with characteristics of database query and advantage of firefly algorithm. Firstly, the firefly group is divided into multiple groups with different parameters, and each group of fireflies followed the optimal firefly in the same group for optimizing. Then, mutual learning mechanism is established among various groups of optimal fireflies to realize inter-group information exchange. At last, query optimization data of database are adopted for the simulation experiment. Experiment results indicate that MGFA is a query optimization method of database with good performance. It can obtain better query result than other algorithms do.

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Acknowledgements

The funding was provided by National Natural Science Foundation of China (Grant No. 61472126).

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, Hunan Province, China

    Yifu Zeng, Yantao Zhou & Fei Zheng

Authors
  1. Yifu Zeng
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  2. Yantao Zhou
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  3. Fei Zheng
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Correspondence to Yifu Zeng.

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Zeng, Y., Zhou, Y. & Zheng, F. Data skyline query protocol based on parallel genetic improvement decision tree. J Supercomput 76, 1116–1127 (2020). https://doi.org/10.1007/s11227-018-2593-1

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  • DOI: https://doi.org/10.1007/s11227-018-2593-1

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