摘要
为了准确地估计反应动力学参数 ,提出一种混沌遗传算法 (chaosgeneticalgorithm ,CGA) ,它基于混沌变量的遗传操作 ,将使子代个体均匀地分布于定义空间 ,从而可避免早熟 ,以较大的概率实现全局最优搜索 .与传统的遗传算法相比较 ,CGA的在线和离线性能都有较大的改进 .将CGA应用于 2 -氯苯酚在超临界水中氧化反应动力学参数的估算 。
As the indivdual distribution of linear crossover operator in real-coded traditional genetic algorithm (GA) tends to approach the center of defined space with the searching process, a novel genetic algorithm, which is named as chaos genetic algorithm (CGA), is proposed. Its genetic operation, which is based on chaos variable, makes the individuals of subgeneration distribute uniformly in the defined space and avoids the premature of subgeneration. To compare the performances of the CGA with those of the traditional GA, the CGA and the traditional GA were applied to estimate the kinetic parameters of 2-chlorophenol oxidation in supercritical water under the same condition. The results demonstrated that the CGA's on-line and off-line performance was all superior to that of the traditional GA, and that the probability of finding global optimal solution was larger than that of the traditional GA. Thus, due to the good performances of the CGA and the drawbacks of the premature nature and the finding part optimal solution of the traditional GA, the CGA is an attractive alternative to the traditional GA.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2002年第8期810-814,共5页
CIESC Journal
基金
国家自然科学基金资助项目 (No .2 0 0 760 41)~~
