增强性人工蜂群算法及在多阀值图像分割中的应用（英文）

被引：10

作者：

高扬 ^{[1
]}

李旭 ^{[2
]}

董明 ^{[1
]}

李鹤鹏 ^{[3
]}

机构：

[1] Academy of Information Technology Northeastern University,Shenyang

[2] Benedictine University

[3] Shenyang Institute of Automation Chinese Academy of

来源：

Journal of Central South University | 2018年 / 25卷 / 01期

关键词：

人工蜂群算法; 局部搜索; 群体智能; 图像分割;

D O I：

暂无

中图分类号：

TP18 [人工智能理论]; TP391.41 [];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ; 080203 ;

摘要：

提出了一种改进的人工蜂群算法来处理图像分割问题,具体采用一系列群体优化觅食策略来平衡开发和探测寻优模式。该算法的主要思想是将局部搜索策略和基于多维粒子群方程的复杂学习策略相结合,可丰富人工蜂群觅食行为模式。通过全局学习,蜂群把全局最优信息整合到搜索方程中以提高探测搜索能力,同时局部搜索使蜂群能更深层探索优势区域,最终取得开发和探索平衡。通过比较该改进蜂群算法和进化算法、群智能算法在一系列基准函数上的实验结果,表明本文所提出的算法的有效性。将改进蜂群算法应用于处理图像分割问题,实验结果也证明了该算法的有效性

引用

页码：107 / 120

页数：14

共 15 条

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