中文摘要：

针对标准文化算法中影响函数仅通过单层信念空间来指导种群进化,易导致双演化文化算法结构失效及全局寻优能力差、不稳定等问题,提出一种基于多层信念空间的文化算法.算法通过对多层信念空间实行分层管理,在提高知识有效性的同时为主群体空间的进化提供最优模式.根据种群分散度自适应调整多层信念空间的融合机制,从而在进化前期维持种群的多样性,在进化后期加速种群收敛.基于典型复杂函数的数值仿真研究表明,该算法在解的精度、稳定性及全局寻优能力等方面较其它同类算法有明显的优势.

英文摘要：

In standard cultural algorithm, the influence function guides the evolution only by a single layer belief space, which may invalidate the slructure of cultural algorithm and lead to poor global optimization and instability. Therefore, a cultural al- gorithm based on multi-layer belief space is proposed. The algorithm increases the validity of knowledge and at the same time pro- rides the optimal mode for the evolution of main population space by managing multilayer belief spaces hierarchically. And adjust the fusion mechanism of the multilayer belief spaces adaptively according to the population dispersion so that it can keep the popula- tion diversity in the early evolution and accelerate population convergence in the late evolution. Aiming at benchmark functions, comparing with some other similar algorithms, the proposed algorithm shows better optimization performance in the precision of the solution, stability and global convergence.