查询结果:   赵旭芳,梁昔明,龙文.基于最优个体指导单纯形法改进的人工蜂群算法及应用[J].计算机应用与软件,2019,36(2):44 - 51,92.
中文标题
基于最优个体指导单纯形法改进的人工蜂群算法及应用
发表栏目
应用技术与研究
摘要点击数
733
英文标题
IMPROVED ARTIFICIAL BEE COLONY ALGORITHM WITH SIMPLEX METHOD BASED ON OPTIMAL SOLUTION AND ITS APPLICATION
作 者
赵旭芳 梁昔明 龙文 Zhao Xufang Liang Ximing Long Wen
作者单位
北京建筑大学理学院 北京102600 贵州财经大学经济系统仿真贵州省重点实验室 贵州 贵阳 550025    
英文单位
School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102600, China Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance and Economics, Guiyang 550025,Guizhou,China    
关键词
人工蜂群算法 单纯形法 最优解 数值试验 参数优化
Keywords
Artificial bee colony algorithm Simplex method The optimal solution Numerical experiments Parameter optimization
基金项目
国家自然科学基金项目(61463009);北京自然科学基金项目(4122022);中央支持地方科研创新团队项目(PXM2013-014210-000173);贵州省科学技术基金项目(黔科合基础[2016]1022);北京建筑大学市属高校科研业务费专项资金项目(X18193);贵州省高校科技拔尖人才支持计划项目(黔科合KY字[2017]070)
作者资料
赵旭芳,硕士生,主研领域:最优化方法及其应用。梁昔明,教授。龙文,教授。 。
文章摘要
针对基本人工蜂群算法在求解复杂优化问题时,存在收敛精度低、收敛速度慢的缺点,提出一种基于最优个体指导单纯形法改进的人工蜂群算法。算法引入基于当前最优个体作为指导的单纯形法进行邻域搜索,以增强局部探索能力。同时采取保优策略,以加快收敛速度。通过6个标准测试优化问题的仿真实验表明,该算法较基本人工蜂群算法具有更高的求解精度和更快的收敛速度。将算法用于分数阶登革病毒传播模型的参数优化,所得的参数对应的模型输出与实际数据拟合情况较好。
Abstract
The basic artificial bee colony (ABC) algorithm has disadvantage of low convergence precision and slow convergence speed in solving complex optimization problems. To solve this problem, we proposed an improved artificial bee colony algorithm with simplex method based on optimal solution. Simplex method, which was based on the current optimal individual as guidance, was introduced to search the neighborhood to enhance the ability of local exploration. The optimization strategy was adopted to accelerate the convergence speed. The simulation results on 6 standard test optimization problems show that compared with the basic ABC algorithm, the improved algorithm has higher solution accuracy and faster convergence speed. The improved algorithm is applied to optimize the parameters of fractional dengue virus propagation model. The output of the model corresponding to the parameters is in better fit with the actual data. 
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