摘要
Differential evolution （DE） is an evolutionary optimization method, which has been successfully used in many practical cases. However, DE involves large computation time, especially, when used to optimize the compurationally expensive objective function. To overcome this .difficulty, the concept of immunity based on vaccination is used to help proliferate excellent schemata and to restrain the degenerate phenomenon. To improve the effective- ness of vaccines, a new vaccine autonomous obtaining method, and a method of deciding the probability of vacci- nation are proposed. In addition, a method for modifying the search space dynamically is proposed to enhance the possibility of converging to the true global optimum. Experiments showed that the improved DE performs better than the classical DE significantly.
微分进化(DE ) 是一个进化优化方法，它成功地在许多实际盒子中被使用了。然而，特别，当过去常优化计算联盟者时， DE 包含大计算时间昂贵的客观功能。克服这个困难，免疫的概念基于种痘被用来帮助增殖优秀模式并且制止退化现象。为了改进决定的疫苗，一个新疫苗的自治获得方法，和一个方法的有效性，种痘的概率被建议。另外，为动态地修改搜索空间的一个方法被建议提高收敛到真全球最佳的可能性。实验证明改进 DE 显著地比古典 DE 更好表现。
基金
Supported by the National Natural Science Foundation of China （60736021）, the National High Technology Research and Development Program of China （2006AA04Z184, 2007AA041406）, and the Key Technologies R＆D Program of Zhejiang Province （2006C 11066, 2006C31051）.
