This work investigates an artificial immune optimization system suitable for single and multi-objective constrained optimization. In this optimizer, an evaluation index, which can decide the importance of individual in the current population, is developed to accelerate population division; the niching-like proliferation scheme is introduced to strengthen the diversity of population. Thereafter, those diverse antibodies, with the help of immune evolution operations, evolve their structures along different directions. Theoretical results show that such optimization system is convergent with low computational complexity. Experimentally, one such optimizer is sufficiently examined by a suite of single and multi-objective test problems. Comparative experiments illustrate that the optimizer with some striking characteristics is a potentially alternative optimization tool for constrained omni-optimization.
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