工程科学与技术2017，Vol.49Issue(5)：127-134,8.DOI:10.15961/j.jsuese.201600671
基于群密度的改进果蝇优化算法及在异常检测中的应用
Improved Fruit Fly Optimization Algorithm Based on Population Density and Its Application in Anomaly Detection
摘要
Abstract
To address the problems that the traditional fi-uit fly algorithms cannot steadily bad converge,and effectively balance the global and local searching ability,a novel frtfit fly optimization algorithm based on population density was proposed.Firstly,by utilizing the advantages of existing methods,the fruit flies were divided into the searching fruit flies and the following fruit flies,which were then used for global searching and local searching,respectively.Secondly,in order to improve the stability of global searching process,the partition sampling strategy based on optimal interval avoidance was used to update the positions of searching fruit flies in each iteration process.The strategy selected the fruit flies with the best performances in each iteration to construct the optimal firuit fly group,and determined the optimal interval according to the ranges of the fruit flies in each dimension.Then,the new positions of the searching fruit flies were determined by sampling the interval except for the optimal interval.Finally,in order to balance the global and local searching ability,the conception of population density was introduced,and the dynamic population size adjustment of different types of fruit flies was achieved by thresholding the population density.The experimental results of typical test functions showed that the partition sampling strategy based on optimal interval avoidance achieved higher global optimization ability compared to traditional rand functions.Compared to traditional optimization algorithms,the proposed algorithm obtained high optimization accuracy and stability while guaranteeing the convergence speed,achieving obvious improvements on comprehensive performances.The simulation results of the anomaly detection showed that,the fruit fly algorithm based on the partition sampling and population density can avoid local optimum effectively,and is effective in obtaining the optimal values of important parameters of the anomaly detection classifier.关键词
果蝇算法/收敛稳定性/全局搜索/局部搜索/异常检测Key words
fruit fly algorithm/convergence stability/global searching/local searching/anomaly detection分类
信息技术与安全科学引用本文复制引用
王友卫,朱建明,凤丽洲..基于群密度的改进果蝇优化算法及在异常检测中的应用[J].工程科学与技术,2017,49(5):127-134,8.基金项目
北京市自然科学基金资助项目(4174105) （4174105）
中央财经大学学科建设基金资助项目(2016XX02) （2016XX02）
国家自然科学基金重点支持项目-NSFC-浙江两化融合联合基金项目资助(U1509214) （U1509214）
