An Improved Particle Swarm Optimizer with Shuffled Sub-swarms and Its Application in Soft-sensor of Gasoline Endpoint

摘要：

This paper proposes a shuffled sub-swarms particle optimizer algorithm (SSPSO) to enhance the diversity of particles in the swarm to improve the performance of PSO. SSPSO is tested with a series of benchmark functions and compared with other version PSO algorithms. Experimental results show that SSPSO improves the search performance on the benchmark functions significantly. Furthermore, SSPSO is used to train NN to construct an artificial neural network SSPSONN. Then SSPSONN is applied to construct a soft-sensor of gasoline endpoint and compared with actual industrial data, the results show that the constructed soft-sensor is feasible and effective.

关键词： Particle swarm optimizer Sub-swarm Shuffled Gasoline endpoint Soft-sensor

作者: Hui Wang Feng Qian

作者单位: State-Key Laboratory of Chemical Engineering, Ecust China University of Science and Technology, Shanghai 200237, P. R. China

会议类型: 国际会议

会议名称: The 2007 International Conference on Intelligent Systems and Knowledge Engineering(第二届智能系统与知识工程国际会议)

会议地点: 成都

会议语种:英文

页码: 1456-1461

在线出版日期: 2007-10-15（万方平台首次上网日期，不代表论文的发表时间）