兰娅勋. 混沌和柯西变异的蝗虫优化算法及特征选择[J]. 微电子学与计算机, 2021, 38(11): 21-30. DOI: 10.19304/J.ISSN1000-7180.2021.0084
引用本文: 兰娅勋. 混沌和柯西变异的蝗虫优化算法及特征选择[J]. 微电子学与计算机, 2021, 38(11): 21-30. DOI: 10.19304/J.ISSN1000-7180.2021.0084
LAN Yaxun. Grasshopper optimization algorithm based on chaos and cauchy mutation and feature selection[J]. Microelectronics & Computer, 2021, 38(11): 21-30. DOI: 10.19304/J.ISSN1000-7180.2021.0084
Citation: LAN Yaxun. Grasshopper optimization algorithm based on chaos and cauchy mutation and feature selection[J]. Microelectronics & Computer, 2021, 38(11): 21-30. DOI: 10.19304/J.ISSN1000-7180.2021.0084

混沌和柯西变异的蝗虫优化算法及特征选择

Grasshopper optimization algorithm based on chaos and cauchy mutation and feature selection

  • 摘要: 传统蝗虫优化算法在处理优化问题时依然存在收敛速度慢、易陷入局部最优的不足.为此,提出了融合混沌映射和柯西变异机制的非线性蝗虫优化算法CCGOA.通过融合混沌Tent映射与反向学习机制,对种群初始化,在确保初始种群质量较优前提下,使种群尽可能均匀分布于搜索空间;利用余弦函数设计非线性自适应系数更新机制,更好均衡个体全局搜索与局部开发能力;引入柯西变异对当前最优个体进行变异扰动,避免算法陷入局部最优.通过基准函数寻优测试,证实提出的算法可以有效提升寻优精度和收敛速度.设计了特征选择算法CCGOA-FS并应用于特征选择问题求解.通过若干数据集测试,证实该算法可以有效进行最优特征子集选取,提升数据分类准确率.

     

    Abstract: There are some shortages when traditional grasshopper optimization algorithm deals with some complicated optimization problems, such as slower convergence speed and more easy to fall into a local optimum. For this reason, a non-linear grasshopper optimization algorithm CCGOA, which combines chaotic mapping and Cauchy mutation mechanism, is proposed. The population is initialized by mixing chaotic Tent map and opposite-learning mechanism, which can ensure better-quality individuals in initial population and make all individuals distributed more uniform in search space as far as possible. A non-linear adaptive coefficient update mechanism based on cosine function is designed to get better trade-off between global search and local development. And, Cauchy mutation is introduced to mutate and disturb the current optimal individual to avoid the algorithm falling into the local optimum. The benchmark function optimization test proves that the algorithm can effectively improve the optimization accuracy and convergence speed. The feature selection algorithm CCGOA-FS is designed and applied to solve the feature selection problem. Several data set tests proved that the algorithm can effectively select the optimal feature subset and improve the accuracy of data classification.

     

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