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Improved hybrid particle swarm optimisation for image segmentation

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成果类型:
期刊论文
作者:
Shuo Liu;Kang Zhou*;Huaqing Qi;Jiangrong Liu
通讯作者:
Kang Zhou
作者机构:
[Shuo Liu; Kang Zhou; Jiangrong Liu] Department of Math and Computer, Wuhan Polytechnic University, Wuhan, People’s Republic of China
[Huaqing Qi] Department of Economics and Management, Wuhan Polytechnic University, Wuhan, People’s Republic of China
通讯机构:
[Kang Zhou] D
Department of Math and Computer, Wuhan Polytechnic University, Wuhan, People’s Republic of China
语种:
英文
关键词:
Hybrid particle swarm optimisation;region equilibrium;compression;factor;image segmentation
期刊:
International Journal of Parallel, Emergent and Distributed Systems
ISSN:
1744-5760
年:
2021
卷:
36
期:
1
页码:
44-50
基金类别:
This work was supported by the National Natural Science Foundation of China [grant numbers 61179032 and 61303116].
机构署名:
本校为第一且通讯机构
院系归属:
数学与计算机学院
经济学院
摘要:
A method for image segmentation based on improved hybrid particle swarm optimisation (PSO) is proposed. In view of the shortcoming that the traditional PSO algorithm is easy to fall into local optimal solution, we update the particle velocity based on the combination of global optimisation, region equilibrium and compression factor. By this way, the searchability of the particle and optimisation performance of the improved PSO is improved. Experiments results on three classic test functions show that the algorithm can greatly improve the search...

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