文献类型：期刊文献

中文题名：量子连续粒子群优化算法及其应用

英文题名：Quantum continuous particle swarm optimization algorithm and its application

作者：吕强[1];陈如清[2];俞金寿[3]

机构：[1]杭州电子科技大学自动化学院,杭州310018;[2]嘉兴学院机电工程学院,嘉兴314001;[3]华东理工大学自动化研究所,上海200237

年份：2008

卷号：28

期号：5

起止页码：122

中文期刊名：系统工程理论与实践

外文期刊名：Systems Engineering-Theory & Practice

收录：CSTPCD;;国家哲学社会科学学术期刊数据库;EI(收录号：20082611337574);Scopus;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

语种：中文

中文关键词：进化算法;粒子群;量子计算;软测量模型

外文关键词：evolutionary algorithm; particle swarm; quantum computing; soft sensing model

摘要：提出了基于量子理论的连续粒子群优化(Continuous Particle Swarm Optimization based on Quantum Methodology,CPSO-QM)算法,主要是采用了量子理论中的叠加态特性和概率表达特性.其中,叠加态特性可以使单个粒子表达更多的状态,潜在地增加了种群的多样性;概率表达特性是将粒子的状态以一定的概率表达出来.在基准函数的实验测试中,对比其它常用算法,结果显示本文提出的算法性能较好.在实际应用中,以丙烯腈反应器作为建模研究对象,提出了三种进化策略,实验结果显示,这三种策略训练的神经网络软测量模型都可以较好地预测丙烯腈的收率.
Continuous particle swarm optimization algorithm based on quantum methodology is propesed in the paper. The algorithm mainly uses superposition characteristic and probability representation. Superposition characteristic can make a single particle present several states. In other words, the characteristic potentially increases population diversity. Probability representation is to make particle＇s state be presented according to a certain probability. Compared with other methods for test function in the experiment, the results demonstrate the proposed algorithm is better and more effective. Additionally, acrylonitrile reactor is used as modeling object in the real application. Three evolutional schemes are used. The experimental results show that the networks trained can the better predict the acrylonitrile yield through using three evolutional schemes.