摘要
传统遗传算法搜索速度慢,容易陷入局部最优解.借鉴遗传算法(GA)的思想,利用正态云模型云滴的随机性和稳定倾向性特点,提出一种新的遗传算法—云遗传算法(CGA).CGA由正态云模型的Y条件云发生器实现交叉操作,基本云发生器实现变异操作.最后,进行了函数优化实验和IIR数字滤波器优化设计,并与标准GA、NQGA、CAGA和LARES等算法进行比较,证明了该算法的有效性,具有一定的参考和应用价值.
Traditional genetic algorithm （GA） easily gets stuck at a local optimum,and often has slow convergent speed.As a novel genetic algorithm,cloud-model-based genetic algorithm （CGA） was originally proposed. CGA is based on both the idea of GA and the properties of randomness and stable tendency of a normal cloud model.In this algorithm, a Y-conditional normal Cloud generator is used as the cross operator of GA, and a basic normal cloud generator is used as the mutation operator. Finally, the experiments of function optimization and ⅡR digital filter design were conducted to compare CGA with standard GA, NQGA, CAGA and LARES.From the simulation results,it is believed that CGA is effective and will become a promising candidate of evolutionary algorithms.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2007年第7期1419-1424,共6页
Acta Electronica Sinica
基金
西南交通大学博士生创新基金(No.2007-3)
关键词
遗传算法
云模型
云遗传算法
函数优化
ⅡR滤波器设计
genetic algorithm
cloud model
cloud genetic algorithm
function opfimization
IIR digital filter design
