Abstract Based on the danger theory, an immune algorithm for dynamic constrained single-objective function optimization is proposed. The key of the algorithm is to construct two functional modules: environmental detection and co-evolution. Relying upon the Antigen Presenting Cells (APCs) infected by distressed or apoptotic cells, the change of the environment is detected and the environmental level is confirmed. The co-evolving scheme based on self-reactive, effective and environmental memory cells is explored. The proposed approach can online detect the change of the environment with the merits of simplicity, flexibility and dynamic runtime. The experimental results show that the proposed approach performs better than the compared algorithms, it has potential use for dynamic constrained optimization problems while achieves the reasonable balance between effect and efficiency.
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2012, Vol. 25 
