Self-adaptive learning based immune algorithm

doi:10.1007/s11771-012-1105-3

Self-adaptive learning based immune algorithm

2012-03-29

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AI

DOI：10.1007/s11771-012-1105-3

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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 1.0x10(7) in average.

Keywords:

immune algorithm

multi-modal optimization

evolutionary computation

immune secondary response

self-adaptive learning

Self-adaptive learning based immune algorithm

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