融合细菌觅食趋化算子的生物地理学优化算法  

Hybrid BBO Algorithm Based on Chemotaxis Operator of Bacterial Foraging Algorithm

作　　者：张新明[1,2] 康强[1] 涂强[1] 程金凤[1] ZHANG Xinming KANG Qiang TU Qiang CHENG Jinfeng(College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China Engineering Technology Research Center for Computing Intelligence ＆ Data Mining of Henan Province, Xinxiang 453007, China)

机构地区：[1]河南师范大学计算机与信息工程学院,河南新乡453007 [2]河南省高校计算智能与数据挖掘工程技术研究中心,河南新乡453007

出　　处：《郑州大学学报（理学版）》2016年第4期44-53,共10页Journal of Zhengzhou University:Natural Science Edition

基　　金：河南省重点科技攻关项目(132102110209);河南省基础与前沿技术研究计划项目(142300410295)

摘　　要：针对高维多阈值彩色图像分割中由于维数高带来阈值搜索困难等问题,提出了一种融合细菌觅食算法(BFO)趋化算子的混合生物地理学算法(hybrid biogeography-based optimization,HBBO).首先构建一种嵌入变异操作的迁移算子,去掉BBO算法原有的变异算子;其次将细菌觅食算法中具有较强局部搜索能力的趋化算子的趋化步长固化为1,将此趋化算子与改进的迁移算子融合,并将精英保留策略换成贪婪选择算子,形成混合BBO算法;最后将HBBO算法应用到高维Tsallis熵多阈值彩色图像分割中.实验结果表明,与目前的MABC、IDPSO、MBFO和BBO-M算法相比,HBBO算法在高维多阈值图像分割中有更好的优化性能、更快的运行速度和更强的稳定性.A hybrid biogeography-based optimization （ HBBO） algorithm based on chemotaxis operator of bacterial foraging algorithm （ BFO） was proposed for solving problems in high dimensional multi-level col-or image thresholding. Firstly, some mutation operations were blended into the original migration operator to get a new migration operator. Secondly, a one-step length chemotaxis operator from BFO was combined with the new migration operator. In addition, a greedy selection method was used to substitute the original elitist selection approach to form a hybrid BBO. Finally, the HBBO algorithm was applied to high dimen-sional Tsallis entropy multilevel color image thresholding. Experimental results of image segmentation showed that the HBBO obtained more significant optimization performance, faster running speed and stronger stability compared with MABC, IDPSO, MBFO and BBO-M.

关 键 词：智能优化算法 生物地理学优化算法 彩色图像分割 多阈值分割 TSALLIS熵 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]