引用本文:陈国初, 俞金寿.两群微粒群优化算法及其应用[J].控制理论与应用,2007,24(2):294~298.[点击复制]
CHEN Guo-chu, YU Jin-shou.Two sub-swarms particle swarm optimization algorithm and its application[J].Control Theory and Technology,2007,24(2):294~298.[点击复制]
两群微粒群优化算法及其应用
Two sub-swarms particle swarm optimization algorithm and its application
摘要点击 1791  全文点击 826  投稿时间：2005-01-18  修订日期：2006-07-17
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DOI编号  
  2007,24(2):294-298
中文关键词  微粒群优化算法  优化  催化裂化装置  轻柴油95%点  软测量
英文关键词  PSO(particle swarm optimization algorithm)  optimization  fluid catalytic cracking unit  light diesel oil  soft-sensor
基金项目  上海市教委自然科学科研项目(05vz01).
作者单位
陈国初, 俞金寿 上海电机学院 电气学院, 上海 200240
华东理工大学 自动化研究所, 上海200237 
中文摘要
      针对微粒群优化算法容易陷入局部极值的缺陷,提出两群微粒群优化算法.通过对5种常用测试函数进行测试和比较,结果表明两群微粒群优化算法比基本微粒群优化算法更容易找到全局最优解,优化效率明显提高.然后将两群微粒群优化算法用于催化裂化装置主分馏塔轻柴油95%点软测量建模,通过与实际工业数据对比,表明该软测量模型具有高的精度、好的性能和广阔的应用前景.
英文摘要
      In order to improve optimization performance of particle swarm optimization algorithm(PSO), a new two sub-swarms particle swarm optimization algorithm(TSPSO) is proposed in this paper. Then, both TSPSO and PSO are used to resolve five well-known and widely used test functions' optimization problems. Results show that TSPSO has greater efficiency and better performance than PSO. TSPSO is also applied to train artificial neural network(NN)to construct a practical soft-sensor for the 95%-point light diesel oil in a main fractionator of fluid catalytic cracking unit(FCCU).The obtained results and comparison with actual industrial data indicate that the proposed method is feasible and effective in soft-sensor for the 95\%-point light diesel oil.