混沌免疫进化算法及其在函数优化中的应用  

Chaos Immune Evolutionary Algorithm and Its Applications to Function Optimization

作　　者：张海英[1] 韩贵金[2] 潘永湘[1] 

机构地区：[1]西安理工大学自动化学院,西安710048 [2]西安邮电学院信息与控制系,西安710121

出　　处：《模式识别与人工智能》2007年第2期225-229,共5页Pattern Recognition and Artificial Intelligence

摘　　要：基于免疫系统的克隆选择机理,并利用混沌序列的遍历性,提出一种混沌免疫进化算法.算法首先将混沌序列引入算法初始群体的产生和抗体的扩展过程.其次将待扩展群体中的个体亲和度进行变换以调节个体的选择概率.最后利用概率分析方法,给出算法的全局收敛性证明.为了验证算法的有效性,将算法应用于函数优化问题.用不同的测试函数进行仿真实验.仿真结果表明该算法具有不易陷入局部最优、解的精度高、收敛速度快等优点.Based on the clonal selection principle in the immune system and utilizing the ergodic property of the chaotic sequence, a chaos immune evolutionary algorithm is proposed. Firstly, the chaotic sequence is introduced into the generation of the initial population and expansion process of the antibody. Secondly, the affinity of antibody ready for expansion in the population is varied to modulate the choose probability. Finally, the algorithm is proved to be convergent by utilizing the method of probability analysis. In order to test the validity of the algorithm, it is applied to solving the problem of function optimization . Simulation experiments are made using several different functions and the results show many virtues of the algorithm , such as avoiding local optima, high precision solution and quick convergence.

关 键 词：混沌序列 免疫算法 克隆选择 优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TP18[自动化与计算机技术—计算机科学与技术]