文献类型：期刊文献

中文题名：A Fuzzy-based Adaptive Genetic Algorithm and Its Case Study in Chemical Engineering

英文题名：A Fuzzy-based Adaptive Genetic Algorithm and Its Case Study in Chemical Engineering

作者：Yang Chuanxin[1];Yan Xuefeng[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：2011

卷号：19

期号：2

起止页码：299

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：20111913962189);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000290608100019)】；CSCD:【CSCD2011_2012】；

基金：Supported by the National Natural Science Foundation of China (20776042), the National High Technology Research and Development Program of China (2007AA04Z164), the Doctoral Fund of Ministry of Education of China (20090074110005), the Program for New Century Excellent Talents in University (NCET-09-0346), the "Shu Guang" Project (095G29), and Shanghai Leading Academic Discipline Project (B504).

语种：英文

中文关键词：fuzzy logic controller; genetic algorithm; artificial immune system; reaction kinetics model

外文关键词：fuzzy logic controller; genetic algorithm; artificial immune system; reaction kinetics model

摘要：Considering that the performance of a genetic algorithm （GA） is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm （FAGA） combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.
Considering that the performance of a genetic algorithm (GA) is affected by many factors and their relationships are complex and hard to be described, a novel fuzzy-based adaptive genetic algorithm (FAGA) combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems. In FAGA, immune theory is used to improve the performance of selection operation. And, crossover probability and mutation probability are adjusted dynamically by fuzzy inferences, which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters. The experiments show that FAGA can efficiently overcome shortcomings of GA, i.e., premature and slow, and obtain better results than two typical fuzzy GAs. Finally, FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.