Big data analysis method adopting grey wolf optimization algorithm

An optimization algorithm and analysis method technology, applied in the field of big data analysis, can solve the problems of large data volume, high computational complexity, long processing time for a single machine, etc., to achieve the effect of improving operating efficiency and high search performance

Inactive Publication Date: 2019-07-16
HUBEI UNIV OF TECH
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Problems solved by technology

Feature selection has been an active research field in the past ten to fifteen years. Due to the large amount of data and the high computational complexity caused by the high dimensionality of the data, the single-machine processing time is too long

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  • Big data analysis method adopting grey wolf optimization algorithm
  • Big data analysis method adopting grey wolf optimization algorithm
  • Big data analysis method adopting grey wolf optimization algorithm

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Embodiment Construction

[0018] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0019] Gray Wolf Optimizer (GWO) was proposed by Australian scholar Mirjalili in 2014. By simulating the social class of gray wolves, the predation tasks such as siege, hunt and attack are assigned to different levels of gray wolves to complete the predation behavior. So as to realize the process of global optimization. At present, GWO, as a new heuristic optimization method, has been successfully applied to optimization problems such as power systems.

[0020] With the explosive growth of data volume, it has become a trend to process these huge data volumes...

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Abstract

The invention discloses a big data analysis method adopting a grey wolf optimization algorithm, each piece of data of an applied big data set is used as a feasible solution, each wolf represents a solution, and a single iteration alpha wolf decides a local optimal solution. And after multiple iterative calculations, the selected alpha wolf represents a globally optimal solution. The method mainlycomprises the steps of setting initial parameters of a wolf algorithm; randomly generating a first-generation wolf group, dividing the first-generation wolf group according to the fitness values, andselecting three optimal wolves as alpha, beta and delta wolves, with all the rest being omega wolves; and when the maximum number of iterations is reached, the output alpha wolf represents the globaloptimal solution. The method has higher performance of searching for the globally optimal solution and is high in calculation speed.

Description

technical field [0001] The invention belongs to multiple technical fields such as machine learning, data mining, image processing, and distributed computing, and relates to a big data analysis method, in particular to a big data analysis method of Spark distributed gray wolf optimization algorithm. Background technique [0002] In some fields such as the Internet, finance, and medicine, data sets with hundreds of millions of records can be generated every day. With the development of information technology, a large amount of information is digitized into data and processed and analyzed by computers. [0003] At present, there are four methods commonly used in big data analysis methods: descriptive analysis, diagnostic analysis, predictive analysis and instructional analysis. A large number of facts show that there are still deficiencies in privacy protection, and the failure to properly handle big data will cause great violations of user privacy. [0004] Feature selection...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 陈宏伟韩麟符恒胡周常鹏阳候乔叶志伟徐慧宗欣露严灵毓
Owner HUBEI UNIV OF TECH
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