摘要
结合云模型理论与免疫克隆选择思想,提出一种新的改进算法-混沌云克隆选择算法(CCCSA).该算法采用混沌初始化生成初始种群以提高初始抗体的质量;通过基本正态云发生器实现抗体的变异操作以改善抗体的多样性.经典函数测试实验和时滞系统的自抗扰控制器参数优化整定仿真实验结果表明,该算法比一般的CSA算法、遗传算法和粒子群算法能更快的找到最优解;其求解精度更高,性能更加稳定.
Inspired from the clonal selection mechanism of immune systems and the theory of the cloud model, a chaos clonal selection algorithm based on cloud model was proposed to improve the algorithm. Firstly, the algorithm adopts chaos initialization to generate the initial population in order to improve the quality of initial antibodies. Secondly, the algorithm employs the basic normal cloud generator to achieve antibodies mutation operation in order to improve the diversity of antibodies. Finally, the simulation test of classical function and the parameters tuning of timedelay system with activedisturbancerejection con troller shows that the algorithm can search for the best solution more quickly. Its precision is higher and its stability is better than the CSA, GA and PSO.
出处
《湖南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2014年第3期101-106,共6页
Journal of Hunan University:Natural Sciences
基金
国家自然科学基金资助项目(61174140)
教育部博士点基金资助项目(20110161110035)
湖南省自然科学基金重点资助项目(13JJA002)
关键词
免疫克隆算法
云模型
混沌初始化
函数优化
自抗扰控制器
immune cloning algorithm
cloud model
chaos initialization
function optimization
active-disturbance-rejection controller
