[1]韩 麟,陈宏伟.基于Spark的灰狼优化算法研究[J].湖北工业大学学报,2019,34(5):60-63.
　HAN Lin,CHEN Hongwei.Research on Grey Wolf Optimization Algorithm Based on the Spark[J].,2019,34(5):60-63.
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基于Spark的灰狼优化算法研究()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
34卷
期数:
2019年第5期
页码:
60-63
栏目:
湖北工业大学学报
出版日期:
2019-10-30

文章信息/Info

Title:
Research on Grey Wolf Optimization Algorithm Based on the Spark
文章编号:
1003-4684(2019)05-0060-04
作者:
韩  麟;  陈宏伟
湖北工业大学计算机学院， 湖北 武汉 430068
Author(s):
HAN Lin;  CHEN Hongwei
School of Computer Science, Hubei Univ. of Tech., Wuhan 430068, China
关键词:
Spark; 特征选择; 灰狼优化算法; 高斯变异
Keywords:
Spark;  feature selection;  grey wolf optimization algorithm;  Gaussian
分类号:
TP301.6
文献标志码:
A
摘要:
高斯变异算子融入灰狼优化算法后具有更好的搜索能力，容易陷入局部最优的缺点得到了一定的改善，并且收敛效率也得到提高。将融入了高斯变异算子的灰狼算法良好的搜索能力与Spark平台的计算能力结合，简化了大数据量和计算的高复杂性，从而使算法更简单、更快速、更好地解决问题并降低存储要求。
Abstract:
The calculation time is lengthy due to the high computational complexity caused by the large amount of data and high dimensions. Aiming at this shortcoming, a feature selection method based on Spark platform for Gaussian variant grey wolf optimization algorithm is proposed. The Gaussian mutation operator, after being integrated into the grey wolf optimization algorithm, has better search ability. The shortcoming of easily falling into local optimum has been improved, and its convergence efficiency has also been increased. The good search ability of the gray wolf algorithm incorporating the Gaussian mutation operator is combined with the computing power of the Spark platform, which simplifies the high complexity of large data volume and calculation, thus making the algorithm simpler, faster and better in solving the problem and reducing storage requirements.

参考文献/References:

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备注/Memo

备注/Memo:
［收稿日期］ 2019-07-05
［基金项目］ 国家自然科学基金（61772180）； 湖北省自然科学基金（2013CFB020）
［第一作者］ 韩   麟(1996-)， 男， 湖北孝感人，湖北工业大学硕士研究生，研究方向为云计算，大数据
［通信作者］ 陈宏伟(1975-)， 男， 湖北武汉人，工学博士，湖北工业大学教授，研究方向为云计算，大数据
更新日期/Last Update: 2019-11-21