Self-adaptive learning based immune algorithm

作     者：许斌 庄毅 薛羽 王洲 

作者机构：Department of Computer Engineering&ScienceNanjing University of Aeronautics&Astronautics Science and Technology on Electron-optic Control Laboratory 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2012年第19卷第4期

页      面：1021-1031页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project(2010ZC13012) supported by the Aviation Science Funds of China 

主　　题：immune algorithm multi-modal optimization evolutionary computation immtme secondary response self-adaptivelearning 

摘      要：A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.