Self-adaptive learning based immune algorithm

• Published in: Journal of Central South University 2012-04, Vol.19 (4), p.1021-1031
• Main Authors: Xu, Bin, Zhuang, Yi, Xue, Yu, Wang, Zhou
• Format: Article
• Language: English
• Subjects: Engineering; Metallic Materials
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-012-1105-3

A self-adaptive learning based immune algorithm (SALIA) is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned problems 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.