| 引用本文: | 王友卫,凤丽洲,朱建明,柴艳妹,吴越.一种基于全局-局部双向驱动的改进果蝇优化算法[J].哈尔滨工业大学学报,2018,50(5):93.DOI:10.11918/j.issn.0367-6234.201707123 |
| WANG Youwei,FENG Lizhou,ZHU Jianming,CHAI Yanmei,WU Yue.An improved fruit fly optimization algorithm based on global-local bidirectional driving[J].Journal of Harbin Institute of Technology,2018,50(5):93.DOI:10.11918/j.issn.0367-6234.201707123 |
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| 摘要: |
| 为解决传统果蝇优化算法过早收敛、结果不稳定等问题，提出一种基于全局-局部双向驱动的果蝇优化新算法.首先，为综合考虑果蝇群体的全局化驱动信息和果蝇个体的局部化驱动信息，引入先进群组和记忆空间的概念，即在每次迭代过程中，将果蝇种群中表现较好的若干只果蝇定义为先进群组，将每只果蝇经过的若干历史最优位置定义为该果蝇的记忆空间.然后，为避免过早收敛问题，考虑先进群组中所有个体的全局化驱动作用，通过顺序选择果蝇位置向量的各个维度实现果蝇位置更新.最后，为避免种群接近收敛时盲目地进行全局搜索，每只果蝇个体将考虑自身认知经验的局部化驱动作用，通过使用轮盘赌策略选择记忆空间中特定位置并向其靠近以跳出局部最优.针对典型测试函数及网络异常检测仿真的实验结果表明：基于全局-局部双向驱动的果蝇优化算法收敛精度高、稳定性好、收敛速度快，适用于处理网络异常检测中的高维、复杂的优化问题.
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| 关键词: 果蝇算法 局部最优 轮盘赌 异常检测 多极值 |
| DOI：10.11918/j.issn.0367-6234.201707123 |
| 分类号:TP301.6 |
| 文献标识码:A |
| 基金项目:北京市自然科学基金(4174105); 国家自然科学基金重点支持项目(U1509214); 中央财经大学学科建设基金(2016XX1,6XX02)；全国统计科研计划重点项目(2017LZ05) |
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| An improved fruit fly optimization algorithm based on global-local bidirectional driving |
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WANG Youwei1,FENG Lizhou2,ZHU Jianming1,CHAI Yanmei1,WU Yue3
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(1. School of information, Central University of Finance and Economics, Beijing 100081,China; 2. School of Science and Engineering, Tianjin University of Finance and Economics, Tianjin 300222, China; 3. School of Insurance, Central University of Finance and Economics, Beijing 100081, China)
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| Abstract: |
| To solve the problems that the traditional fruit fly algorithms fall into convergence too early and the results are not stable, an improved fruit fly optimization algorithm based on global-local bidirectional driving is proposed. Firstly, in order to comprehensively consider the global driving information of fruit fly population and the local driving information of a fruit fly individual, the conceptions of advanced group and memory space are introduced. In each iteration, the fruit flies which have good performances are defined as the advanced group, and the historical best positions of a fruit fly are defined as the memory space of this fruit fly. Secondly, in order to avoid the premature convergence problem, the global driving effect of the fruit flies in the advanced group is considered, and the dimensional components of the fruit fly position vectors are updated sequentially in the position updating processes. Finally, in order to avoid the blind global searching when the population approaches convergence, each fruit fly will consider the local driving effect of its own cognitive experience, and the roulette strategy is used to select the positions in the memory space for jumping out the local optimum. The experimental results of typical test functions and the web anomaly detection simulation show that, the proposed fruit fly optimization algorithm based on global-local bidirectional driving has high searching accuracy, good stability, and high convergence speed, and is suitable for dealing with the complex problems with high dimensions in web anomaly detection.
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| Key words: fruit fly algorithm local optimum roulette anomaly detection multiple extremes |
