Objects extraction and classification based on an improved cat swarm optimization algorithm

被引：0
作者：
Zeng, Zhigao [1 ]
Yang, Fanwen [1 ]
Yi, Shengqiu [1 ]
Wen, Zhiqiang [1 ]
Liu, Lihong [1 ]
Guan, Lianghua [1 ]
机构：
[1] College of Computer and Communication, Hunan University of Technology, Zhuzhou
来源：
Journal of Information and Computational Science | 2015年 / 12卷 / 13期
基金：
中国国家自然科学基金;
关键词：
Cat Swarm Optimization; Extraction; Feature Selection; Object; Swarm Intelligent Algorithm;
D O I：
10.12733/jics20106485
中图分类号：
学科分类号：
摘要：
The convergence time of some traditional swarm-intelligence-based algorithms are often overly long when they are used to extract and classify objects in an image. Therefore, an improved cat swarm optimization algorithm is proposed to solve the problem in this paper. In order to improve the convergence speed of the cat swarm optimization algorithm, the velocity updating formula is modified with a nonlinear inertia weight and linear acceleration coefficients. Simultaneously, in order to improve the search speed, the elite reserve strategy is used in the new algorithm. Experiments show that the improved cat swarm optimization algorithm is more effective than other traditional swarm-intelligence-based algorithms when it is used to extract and classify objects in an image. ©, 2015, Journal of Information and Computational Science. All right reserved.
引用
收藏
页码：5053 / 5061
页数：8
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