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× 107 in average.