Abstract: This paper proposed a new dynamic multi-object artificial immune system model based on simulating the principle of the biological immune system. The model consists of five elements, i. e. environment set, antibody set, antigen set, rule set and a Dynamic Multi-object Evolutionary Immune Algorithm(DMEIA). As a key clement of the model, DMEIA combines the feature of evolutionary algorithm which selects optimal non-dominated antibodies and makes them to join in evolution of next generation,and the characteristic of immune algorithm which has strong population diversity and adaptive searching ability to control and assorts with the operation of the model. Compared with the existed algorithms, DMEIA has better convergence, diversity, distribution of solution and stability of environment tracking, therefore the performance of the new model is proven to be available.

Key words: Dynamic mufti objective optimization, Evolutionary immune, Environment tracking, Clone selection