摘要
针对帝王蝶优化算法(MBO)全局搜索能力较弱、在迁移过程中容易出现种群多样性减少等问题,文中提出基于柯西变异的差分自适应MBO及其特征选择算法.首先,使用差分进化算法中的变异操作替换MBO的迁移算子,提升全局搜索能力.然后,将自适应调整策略融入MBO的调整算子,改变单一的调整方式.最后,对每次更新的种群进行柯西变异,增加种群多样性.为了验证改进帝王蝶优化算法及其特征选择方法的性能,通过基准函数和UCI数据集两部分实验对其进行测试,结果表明文中算法性能较优.
Aiming at the weak global search ability and the reduction of population diversity during migration of monarch butterfly optimization(MBO)algorithm,a differential adaptive MBO algorithm based on Cauchy mutation and its feature selection method are proposed.Firstly,the MBO migration operator is replaced by the mutation operation in the differential evolution algorithm to improve the global search ability.Then,MBO adjustment operator is combined with the adaptive adjustment strategy to change the single adjustment mode.Finally,Cauchy mutation is conducted in each updated population to increase population diversity.To verify the performance of the improved MBO algorithm and its feature selection method,experiments on benchmark functions and UCI datasets are conducted,and the results show that the proposed algorithms produce better performance than other algorithms.
作者
孙林
赵婧
徐久成
薛占熬
SUN Lin;ZHAO Jing;XU Jiucheng;XUE Zhan′ao(College of Computer and Information Engineering,Henan Nor-mal University,Xinxiang 453007;Engineering Laboratory of Intelligence Business and Internet of Things Technologies,Henan Normal University,Xinxiang 453007)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2020年第11期981-994,共14页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.62076089,61772176,61402153,61976082)
河南省科技创新人才项目(No.184100510003)
河南省高校青年骨干教师培养计划项目(No.2017GGJS041)
河南省高等学校重点科研项目(No.21A520020、21A520023)资助。
关键词
特征选择
帝王蝶优化算法(MBO)
差分进化算法
柯西变异
Feature Selection
Monarch Butterfly Optimization(MBO)Algorithm
Differential Evolution Algorithm
Cauchy Mutation
