In order to increase the diversity of immune algorithm when solving global optimization problems, a novel
immune evolutionary algorithm(IEA) is proposed. The main characteristics of IEA are clonal expansion and multiple-parent
random receptor editor operators. In addition, a modified hypermutation operator is introduced to improve the learning ability
of individuals. Particularly, a novel performance evaluation criterion is constructed, by which the performance of different
algorithms can be compared easily. In the experimental study, the ratio of clonal expansion is determined, and the IEA is
compared with fast clonal algorithm(FCA) and Opt-IMMALG. The results show that IEA is significantly better than FCA
and Opt-IMMALG.