大数据环境中群智能算法的应用研究  

Application research on group intelligence algorithm in the background of big data

作　　者：张敏辉 杨剑[2] ZHANG Minhui;YANG Jian(School of Computer Science,Chengdu Normal University,Chengdu 611130,China;Computer Department,Chengdu College of University of Electronic Science and Technology of China,Chengdu 611731,China)

机构地区：[1]成都师范学院计算机科学学院,成都611130 [2]电子科技大学成都学院计算机系,成都611731

出　　处：《智能计算机与应用》2018年第4期81-84,共4页Intelligent Computer and Applications

基　　金：四川省教育厅重点科研项目(17ZA0053)

摘　　要：随着科技的快速发展,大数据时代已经到来。对于大数据的分析与处理推动社会经济的不断发展,在大数据背景下,数据规模、处理难点的优化问题也变得更加多样化,进而使优化方法成为人们日益关注的焦点。一种新型的计算技术——群智能算法,运用高效的优化算法对自然界社会性生物群体进行模拟,解决各个领域的实际问题。本文提出群智能算法中的自适应优化算法——粒子群算法,详细分析粒子群算法的原理,为了提高全局搜索能力及计算效率,本文加入了种群自适应增加/删除个体数目方法有效改进种群多样化,提高收敛速度及质量。基于逻辑斯谛模型的算子设计有效地增强了粒子群的多样性,自适应控制策略更具有一般性特征,可更好地应用到不同的群智能算法中,解决大数据问题的优化性。With the rapid development of science and technology,the era of big data has come. The analysis and processing of big data will promote the continuous development of society and economics. In the background of big data,the optimization of data size and processing difficulties has become more diversified,and the optimization method has become the focus of people＇s attention. A new computing technology,group intelligence algorithm,is used to simulate the social biological groups in nature by using efficient optimization algorithms to solve practical problems in various fields. In this paper,an adaptive optimization algorithm,particle swarm optimization（ PSO） algorithm,is proposed. The principle of particle swarm optimization is analyzed in detail. In order to improve the global search ability and efficiency,this paper adds a population adaptive increase/delete individual number method to improve the population diversity and improve the convergence speed and quality. The operator design based on the logistic model can effectively enhance the diversity of the particle swarm. The adaptive control strategy has more general characteristics,and can be better applied to different swarm intelligence algorithms for better solving the optimization of big data problems.

关 键 词：大数据 智能算法 优化 粒子群 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]