基于改进人工蜂群算法的大数据优化  

Big Data Optimization Based on Improved Artificial Bee Colony Algorithm

作　　者：南楠 严英占[2] NAN Nan;YAN Ying-zhan(Basic Education College, Lingnan Normal University, Zhanjiang Guangdong 524048, China;China Electronics Technology Group 54th Institute, Shijiazhuang 050081, China)

机构地区：[1]岭南师范学院基础教育学院,广东湛江524048 [2]中国电子科技集团第54研究所,石家庄050081

出　　处：《西南师范大学学报（自然科学版）》2021年第3期20-26,共7页Journal of Southwest China Normal University(Natural Science Edition)

基　　金：河北省教育厅青年基金项目(QN2016182).

摘　　要：针对传统方法无法解决具有5 V独特属性的大数据优化问题,提出基于改进人工蜂群(Artificial Bee Colony,ABC)算法的大数据优化信号重构算法.该算法通过引导所考虑问题的现有信息来初始化食物源,在引领蜂阶段使用交叉和变异算子生成候选解,并使用轮盘赌反向选择机制生成要交叉的食物源,观察蜂采用Rechenberg 1/5变异规则来自适应地控制扰动大小,在全局最优解的邻域内提供固定的搜索操作.实验结果表明:与其他方法相比,本文算法具有更稳健的最优和平均最优目标函数值,对大数据优化问题能够产生令人满意的结果.To solve the problem of traditional methods without solving the big data optimization problem with 5V unique attributes,an improved artificial bee colony(ABC)algorithm has been proposed to optimize the big data signal reconstruction algorithm.The algorithm initializes the food source by guiding the existing information of the problem under consideration,then uses the crossover and mutation operators to generate candidate solutions in the leading bee stage,and uses the roulette selection mechanism to generate the food sources to be crossed.Finally,the bee adopts Rechenberg 1/5 mutation rule to adaptively control the size of the perturbation,and provide a fixed search operation in the neighborhood of the global optimal solution.The experimental results show that compared with other methods,the proposed algorithm has more robust optimal and average optimal objective function values,which can produce satisfactory results for big data optimization problems.

关 键 词：大数据优化 人工蜂群算法 全局最优解 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]