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两群替代微粒群优化算法及其应用
引用本文:陈国初,俞金寿,郭伟.两群替代微粒群优化算法及其应用[J].医学教育探索,2005(6):787-791.
作者姓名:陈国初  俞金寿  郭伟
作者单位:华东理工大学自动化研究所,上海200237
摘    要:提出一种两群替代微粒群优化算法(TSSPSO),并对算法参数进行分析和对算法方程进行修正。该方法将微粒分成飞行方向不同的两分群,其中一分群微粒朝着最优微粒飞行,另一分群微粒朝着相反方向飞行;飞行时,每一微粒不仅受到微粒本身飞行经验和本分群最优微粒的影响,还受到全群最优微粒的影响。搜索时,每一次迭代均以一定的替代率用一分群中若干优势微粒取代另一分群中相同数目的劣势微粒。对4种常用函数的优化问题进行测试并进行比较,结果表明:两群替代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,优化效率和优化性能明显提高。将两群替代微粒群优化算法用于常压塔汽油干点软测量,建立基于两群替代微粒群优化算法的汽油干点神经网络软测量模型,通过与实际工业数据的比较,表明基于两群替代微粒群神经网络的软测量模型精度高、性能好。

关 键 词:微粒群优化算法(PSO)  两群替代微粒群优化算法(TSSPSO)  优化  汽油干点  软测量
收稿时间:2004/11/24 0:00:00

Two Sub-swarms Substituting Particle Swarm Optimization Algorithm and Its Application
CHEN Guo-chu,YU Jin-shou,GUO Wei.Two Sub-swarms Substituting Particle Swarm Optimization Algorithm and Its Application[J].Researches in Medical Education,2005(6):787-791.
Authors:CHEN Guo-chu  YU Jin-shou  GUO Wei
Institution:Research Institute of Automation ,East China University of Science and Technology ,Shanghai 200237, China
Abstract:In this paper,two sub-swarms substituting particle swarm optimization algorithm(TSSPSO) is proposed.The algorithm parameters are analyzed and the iteration equations are amended.The new algorithm assumes that particles are divided into two sub-swarms.One sub-swarm flies toward the global best particle.and the other flies in the opposite direction.Not only its search experience and the best individual's position of its own sub-swarm,but also the best individual's position of the whole swarm can affect each particle's search during iterations.Each iteration,some bad particles of one sub-swarm are replaced with some good particles of another under a substituting probability.Then,both TSSPSO and particle swarm optimization algorithm(PSO)are used to resolve four well-known and widely used test functions' optimization problems.Results show that TSSPSO has greater efficiency,better performance and more advantages than PSO in many aspects.In addition,TSSPSO is applied to train artificial neural network to construct a practical soft-sensor of gasoline endpoint of crude distillation unit.The obtained results and comparison with actual industrial data indicate that the new method is feasible and effective in soft-sensor of gasoline endpoint.
Keywords:particle swarm optimization algorithm  two sub-swarms substituting particle swarm opti-mization algorithm  optimization  gasoline endpoint  soft-sensor
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