An improved discrete optimization algorithm based on artificial fish swarm and its application for attribute reduction

被引：0

作者：

Ni, Zhiwei ^{[1
,2
]}

Zhu, Xuhui ^{[1
,2
]}

Ni, Liping ^{[1
,2
]}

Cheng, Meiying ^{[1
,2
]}

Wang, Yiling ^{[1
]}

机构：

[1] School of Management, Hefei University of Technology, Hefei

[2] Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education, Hefei

来源：

Journal of Information and Computational Science | 2015年 / 12卷 / 06期

关键词：

AFSA; Attribute reduction; Discretization; Fractal dimension; Improved discrete artificial fish swarm algorithm;

D O I：

10.12733/jics20105617

中图分类号：

学科分类号：

摘要：

The discrete Artificial Fish Swarm Algorithm (AFSA) has some defectives, such as falling into local optimum value, converging slowly. In order to overcome these shortcomings, an improved discrete optimization algorithm based on artificial fish swarm (IDAFSA) is proposed by improving swarming and preying behavior and moving way for making it discrete, and introducing a strategy of overcoming the local optimum. In addition, the paper strengthens the theoretical basis of the algorithm by proving the global convergence. And then, this algorithm which was combined with fractal dimension is applied to attribute reduction problem. Finally, analyzing the parameters in detail and comparing with other literatures by testing six UCI datasets, and the experimental results show that the method has relatively high feasibility and effectiveness. Copyright © 2015 Binary Information Press.

引用

页码：2143 / 2154

页数：11

共 18 条

[1]

Meng X., Ci X., Big data management: Concepts, techniques and challenges, Journal of Computer Research and Development, 50, 1, pp. 146-169, (2013)

[2]

Feng Z., Guo X., Zeng D., On the research frontiers of business management in the context of big data, Journal of Management Science in China, 16, 1, pp. 1-9, (2013)

[3]

Wang G., Jiang P., Survey of data mining, Journal of Tongji University, 32, 2, pp. 246-252, (2004)

[4]

Huang J., Pan H., Wan Y., Application research on the technology of data mining, Computer Engineering and Applications, 2, pp. 45-48, (2003)

[5]

Li X., Qian J., Artificial Fish-swarm algorithm: The bottom-up development process, Proceedings of the Process Systems Engineering in 2001

[6]

Li X., Shao Z., Qian J., An optimizing method based on autonomous animals: Fishswarm algorithm, Systems Engineering-Theory and Practice, 22, 11, pp. 32-38, (2002)

[7]

Zeng J., Zhang J., Wang S., A discretization method based on artificial Fish- Swarm algorithm, Pattern Recognition and Artificial Intelligence, 19, 5, pp. 611-616, (2006)

[8]

Nie H., Lv P., Qiao Y., Application of improved artificial fish school algorithm in transmission network planning, Proceeding of the CSU-EPSA, 22, 2, pp. 93-98, (2010)

[9]

Ma X., Liu N., Improved artificial Fish-swarm algorithm based on adaptive vision for solving the shortest path problem, Journal on Communications, 35, 1, pp. 1-6, (2014)

[10]

Wang X., Yang J., Teng X., Et al., Feature selection based on rough sets and particle swarm optimization, Pattern Recognition Letters, 28, 4, pp. 459-471, (2007)

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