摘要
受生物免疫系统相关原理的启发,提出一种基于免疫原理的优化方法,用于求解多峰值函数的多个优化解。该方法主要利用克隆选择和阴性选择原理来学习抗原的特性,实现对抗体的促进和抑制。通过对几个常用测试函数的仿真实验,并与其它免疫优化方法进行比较,结果表明该方法具有较强的搜索能力,能快速地搜索到多个峰值,且实现简单。
On the basis ofbiology immune principles, a novel optimization algorithm is proposed for solving many optimal solutions to multimodal function. The algorithm mainly utilized clonal selection and negative selection principles to learn the characteristics of antigens. Thus the diversity of antibodies is maintained with the promotion and suppression. The algorithm is performed on two common test functions and compared to other immune optimization algorithm. The simulation results show that the algorithm has excellent searching ability, and it can fast find possibly many peaks and be realized simply.
出处
《计算机工程与设计》
CSCD
北大核心
2006年第23期4465-4466,4474,共3页
Computer Engineering and Design
基金
重庆市教委科学技术研究基金项目(KJ050802)
重庆师范大学科研基金项目(05xly003)
关键词
克隆选择
阴性选择
亲和力
多峰值函数
优化
clonalselection
negative selection
affinity
multimodalfunction
optimization
