文献类型：期刊文献

中文题名：A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace

英文题名：A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace

作者：Nian Xiaoyu[1];Wang Zhenlei[1];Qian Feng[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2013

卷号：21

期号：5

起止页码：537

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：CHINESE JOURNAL OF CHEMICAL ENGINEERING

收录：CSTPCD;;EI(收录号：20132516431592);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000320294300009)】；CSCD:【CSCD2013_2014】；

基金：Supported by the Major State Basic Research Development Program of China (2012CB720500), the National Natural Science Foundation of China (U1162202), the National Natural Science Foundation of China (61174118), the National High Technology Research and Development Program of China (2012AA040307), Shanghai Key Technologies R&D program (12dz1125100) and the Shanghai Leading Academic Discipline Project (B504).

语种：英文

中文关键词：group search optimization;differential evolution;ethylene and propylene yields;cracking furnace

外文关键词：group search optimization; differential evolution; ethylene and propylene yields; cracking furnace

摘要：To find the optimal operational condition when the properties of feedstock changes in the cracking furnace online,a hybrid algorithm named differential evolution group search optimization(DEGSO) is proposed,which is based on the differential evolution(DE) and the group search optimization(GSO).The DEGSO combines the advantages of the two algorithms:the high computing speed of DE and the good performance of the GSO for preventing the best particle from converging to local optimum.A cooperative method is also proposed for switching between these two algorithms.If the fitness value of one algorithm keeps invariant in several generations and less than the preset threshold,it is considered to fall into the local optimization and the other algorithm is chosen.Experiments on benchmark functions show that the hybrid algorithm outperforms GSO in accuracy,global searching ability and efficiency.The optimization of ethylene and propylene yields is illustrated as a case by DEGSO.After optimization,the yield of ethylene and propylene is increased remarkably,which provides the proper operational condition of the ethylene cracking furnace.
To find the optimal operational condition when the properties of feedstock changes in the cracking furnace online, a hybrid algorithm named differential evolution group search optimization (DEGSO) is proposed, which is based on the differential evolution (DE) and the group search optimization (GSO). The DEGSO combines the advantages of the two algorithms: the high computing speed of DE and the good performance of the GSO for preventing the best particle from converging to local optimum. A cooperative method is also proposed for switching between these two algorithms. If the fitness value of one algorithm keeps invariant in several generations and less than the preset threshold, it is considered to fall into the local optimization and the other algorithm is chosen. Experiments on benchmark functions show that the hybrid algorithm outperforms GSO in accuracy, global searching ability and efficiency. The optimization of ethylene and propylene yields is illustrated as a case by DEGSO. After optimization, the yield of ethylene and propylene is increased remarkably, which provides the proper operational condition of the ethylene cracking furnace.