An agent-based immune evolutionary learning algorithm and its application

Based on the immune theory of biology, a novel evolutionary algorithm, an agent-based immune evolution learning algorithm (AIEL) is proposed. In AIEL, immune mechanics and multi-agent technology are combined to overcome premature problem and to efficiently use the agent ability of sensing and acting on the environment. AIEL integrates global and local search during the searching process. By an application of the algorithm to the optimization of test functions, it is shown that the algorithm outperforms the other algorithms in these benchmark functions. Furthermore, AIEL is applied to determine the murphree efficiency of the distillation column, and satisfactory results are obtained.