Improved hybrid particle swarm optimisation for image segmentation

被引：4
|
作者：
Liu, Shuo [1 ]
Zhou, Kang [1 ]
Qi, Huaqing [2 ]
Liu, Jiangrong [1 ]
机构：
[1] Wuhan Polytech Univ, Dept Math & Comp, Wuhan 430023, Hubei, Peoples R China
[2] Wuhan Polytech Univ, Dept Econ & Management, Wuhan, Hubei, Peoples R China
来源：
INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS | 2021年 / 36卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Hybrid particle swarm optimisation; region equilibrium; compression factor; image segmentation; IMPROVED PSO;
D O I：
10.1080/17445760.2019.1689568
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
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 searchability. Experiments also show that it performs well on image segmentation. [GRAPHICS] .
引用
收藏
页码：44 / 50
页数：7
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