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变邻域宽度的爬山微粒群优化算法及其应用
引用本文:陈国初,俞金寿.变邻域宽度的爬山微粒群优化算法及其应用[J].化工学报,2005,56(10):1928-1931.
作者姓名:陈国初  俞金寿
作者单位:华东理工大学自动化研究所,上海 200237
摘    要:微粒群优化算法(particle swarm optimization algorithm,PSO)是由Kennedy和Eberhart 1995年提出的进化计算算法.PSO简单且具有许多良好的优化性能,但对一些复杂优化问题存在容易陷入局部极值的缺陷.本文提出一种变邻域宽度的爬山微粒群优化算法(hill-climbing PSO with variable width neighborhood,vwnHCPSO),并用5种测试函数进行测试和比较,然后将vwnHCPSO用于催化裂化装置(FCCU)主分馏塔轻柴油闪点软测量.

关 键 词:微粒群优化算法  爬山搜索  邻域  优化  催化裂化装置  轻柴油闪点  软测量靠
文章编号:0438-1157(2005)10-1928-04
收稿时间:11 15 2004 12:00AM
修稿时间:2004-11-152005-01-11

Hill-climbing particle swarm optimization algorithm with variable width neighborhood and its application
CHEN Guochu,YU Jinshou.Hill-climbing particle swarm optimization algorithm with variable width neighborhood and its application[J].Journal of Chemical Industry and Engineering(China),2005,56(10):1928-1931.
Authors:CHEN Guochu  YU Jinshou
Affiliation:Research Institute of Automation, East China University of Science anld Technology, Shanghai 200237, China
Abstract:This paper proposes a hill-climbing particle swarm optimization algorithm with variable width neighborhood (vwnHCPSO).The new method assumes that some stochastic particles are produced in an initial neighborhood of the best particle Pg at the first iteration of PSO.Then the best individual Pgn of the stochastic particles is found. If Pgn is better than Pg,Pg is replaced with Pgn and the next iteration of PSO goes on. If Pgn is not better than Pg,the neighborhood width of the best particle Pg is broadened, the stochastic particles production is renewed and the best individual Pgn of stochastic particles is found again. If Pgn can be better than Pg now, Pg is replaced with Pgn and the next iteration of PSO can go on. Otherwise, the neighborhood width of the best particle Pg is broadened again and the next iteration of PSO does not go on until the best individual Pgn of stochastic particles is found or the neighborhood width exceeds the scheduled width. Then, vwnHCPSO, hill-climbing particle swarm optimization algorithm with invariable width neighborhood (HCPSO) and PSO are used to resolve several well-known and widely used test functions’ optimization problems. Results show that vwnHCPSO has greater efficiency, better performance and more advantages in many aspects than HCPSO and PSO. Next, vwnHCPSO is used to train artificial neural network (NN) to construct a practical soft-sensor of light diesel oil flash point of the main fractionator of fluid catalytic cracking unit (FCCU).The obtained results and comparison with actual industrial data indicate that the new method proposed in this paper is feasible and effective in soft-sensor of light diesel oil flash point.
Keywords:PSO  hill-climbing search  neighborhood  optimization  fluid catalytic cracking unit  light diesel oil flash point  soft-sensor
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