摘要
为了协调算法的勘探和开采能力,提出一种自适应调整子种群个体数目的遗传算法.该算法首先采用佳点集方法初始化种群以保证个体均匀分布在搜索空间中.基于个体的适应度将种群分为3个子种群,并分别采用不同的交叉和变异算子.在进化过程中,根据不同的搜索阶段自适应动态调整各子种群个体的数目.几个标准测试函数的实验结果表明该算法具有较好的寻优性能.将新算法应用到重油热解模型参数估计中,可以获得满意的结果.
In order to coordinate the exploitation ability and development ability of the algorithm, a genet- ic algorithm of individuals number of subpopulation with adaptively adjustment was proposed. A fine-point set method was employed to conduct population initialization in order to guarantee that the individuals were scattered uniformly over entire search space. The population was divided into three subpopulations according to individuals adaptability and different crossover operators and mutation operators were selected for them respectively. In the process of evolution, the individuals number of the subpopulation was dy- namically and adaptively adjusted at different search phases. The experimental result of several benchmark functions showed that this algorithm exhibited a better ability of optimum searching. By using the new al- gorithm for parameter estimation of heavy oil thermal cracking model, a satisfactory result could be obtained.
出处
《兰州理工大学学报》
CAS
北大核心
2013年第4期80-84,共5页
Journal of Lanzhou University of Technology
基金
贵州省科学技术基金(黔科合J字[2013]2082号)
关键词
遗传算法
自适应
重油热解模型
参数估计
genetic algorithm
adaptation
heavy oil thermal cracking model
parameter estimation
