基于人工免疫的多峰函数优化算法研究  

Research on Multi-modal Function Optimization Algorithms Based on Artificial Immune

作　　者：朱葛俊[1] 

机构地区：[1]常州机电职业技术学院,江苏常州213164

出　　处：《计算机仿真》2012年第5期239-242,共4页Computer Simulation

基　　金：2008年度江苏省高校科研成果产业化推进项目(HDZ08-56)

摘　　要：研究函数优化问题,提高寻优效率。针对寻找函数最优解,当函数是具有多个峰值的函数时,传统的基于蚁群的函数优化算法易陷入局部极小,不能找到真正的全局最优解的问题。提出一种基于人工免疫的改进蚁群函数优化方法,在传统的蚁群算法基础上引入人工免疫的思想,加入抗体浓度作为蚂蚁选择下一条路径时的条件,由空间信息素、抗体浓度以及抗体适应度值等全局信息来决定抗体被选择的概率,避免了只依赖单一的信息素寻优而陷入局部极小的问题。实验证明,这种方法不仅能解决多峰函数寻优易陷入局部极小的问题,而且具有很高的迭代寻优效率,取得了满意的结果。Research function optimization problems and improve the optimal efficiency. This paper presented an improved ant colony optimization method based on artificial immune function. The method joined antibody concentration as the conditions under which the ants chose next path. Then using the global information such as space information, antibody concentration and antibody fitness value, the selected probability of antibody was determined, which avoided to fall into the local minimum caused by only rely on a single pheromones optimization. Experiments show that the method can not only solve the problem that multi-modal function optimization easily get into local minimum, but also have high iterative optimization efficiency, and obtaine satisfactory results.

关 键 词：蚁群算法 人工免疫 函数优化 

分 类 号：TP202.7[自动化与计算机技术—检测技术与自动化装置]