文献类型：期刊文献

英文题名：Chaos-genetic algorithms for optimizing the operating conditions based on RBF-PLS model

作者：Yan, Xuefeng F.[1]; Chen, Dezhao Z.[2]; Hu, Shangxu X.[2]

机构：[1]Zhejiang Univ, Dept Chem Engn, Hangzhou 310027, Peoples R China;[2]E China Univ Sci & Technol, Automat Inst, Shanghai 200237, Peoples R China

年份：2003

卷号：27

期号：10

起止页码：1393

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：2003367618060);WOS:【SCI-EXPANDED(收录号:WOS:000184994500003)】；

语种：英文

外文关键词：chaos-genetic algorithms; chaotic variable; optimization; radial basis functions; partial least squares; model

摘要：A novel genetic algorithm (GA) including chaotic variable named chaos-genetic algorithm (CGA) was proposed. Due to the nature of chaotic variable, i.e. pseudo-randomness, ergodicity and irregularity, the evolutional process of CGA makes the individuals of subgenerations distributed ergodically in the defined space and circumvents the premature of the individuals of subgenerations. The performance of CGA was demonstrated through two examples and compared with that by the traditional genetic algorithms (TGA). The results showed the superior performances of CGA over TGA, and moreover, the probability of finding the global optimal value by using CGA is larger than that by using TGA. To illustrate the performance of CGA further, it was employed to optimize the operating conditions of aromatic hydrocarbon isomerization (AHI) process modeled by the radial basis functions (RBF) coupled with partial least squares (PLS) approach. Satisfactory results were obtained. Further, a generalized methodology, which employs RBF-PLS approach to model the complex chemical process based upon practical observation data and subsequently applies CGA to find the optimal operating conditions, was suggested too. (C) 2003 Elsevier Science Ltd. All rights reserved.