关键词: 多方法协作, 免疫进化算法, 多种群, 模式搜索法

Abstract: In order to improve the global search capability of Immune Evolutionary Algorithm（IEA） and reduce its computational complexity, a multi-method collaborative optimization algorithm（MCIEA） is proposed. IEA is improved. Considering individual specificity, antibody population is divided into three subgroups：elite, ordinary, and inferior. By applying Gaussian mutation, uniform mutation, and regeneration to these subgroups respectively, the global search capability of the algorithm is enhanced. In the Pattern Search Method（PSM）, greedy descent is used instead of alternate operation as the move strategy of detection and pattern, which accelerates the convergence speed. PSM is embedded in the process of IEA as the local optimizer and uses the evolutionary information of IEA to set the parameters, e.g. initial point and steps, so as to realize the collaboration between IEA and PSM. Several classical test functions and a layout optimization example of satellite electronic equipment are used to verify the proposed method. The results show that the optimization capability and convergence efficiency of MCIEA are more advanced than IEA, and the computational complexity is decreased significantly.

Key words: multi-method collaboration, immune evolutionary algorithm, multi-population, pattern search method