摘要
针对无监督分类问题,提出一种多尺度并行免疫克隆优化聚类算法.算法中,进化在多个子群之间并行进行,不同子群的抗体根据子群适应度采用不同变异尺度.进化初期,利用大尺度变异子群实现全局最优解空间的快速定位,同时变异尺度随着适应值的提升逐渐降低;进化后期,利用小尺度变异子群完成局部解空间的精确搜索.将新算法与其他聚类算法进行比较,所得结果表明新算法具有较好的聚类性能和鲁棒性.
A novel multi-scale parallel artificial immune clone algorithm for unsupervised clustering（MSPAICC） is presented,in which,evolutions of subgroups are performed in parallel with the different mutation strategies.The mutation capability of an individual is determined by the competition among subgroups and subgroup fitness value.The larger mutation operator is used to quickly localize the global optimal space at the early evolution,while the smaller mutation operator whose scale gradually reduces are adopted to improve the local search ability at the later evolution.The experimental results show the proposed method can improve clustering performance and the robustness compared with other clustering algorithms.
出处
《控制与决策》
EI
CSCD
北大核心
2012年第6期819-826,共8页
Control and Decision
基金
国家自然科学基金项目(61074076)
中国博士后科学基金项目(20090450119)
中国博士点新教师基金项目(20092304120017)
黑龙江省博士后基金项目(LBH-Z08227)
关键词
聚类算法
免疫克隆优化
变异算子
子群适应度
clustering algorithm
immune clone optimization
mutation operator
subgroup fitness value
