元胞空间结构下的文化算法

针对以往文化算法种群空间没有地域的概念，信念空间缺少文化的进化机制，以及求解优化问题时寻优精度不高且易陷入局部最优等缺陷，提出一种新的基于元胞空间结构的文化算法. 将元胞空间网格分别嵌入文化算法计算框架中的种群空间和信念空间以模拟文化算法的双层进化体系；对于种群空间，将进化个体分布于下层元胞网格，并对网格进行地域划分，使每个地域内的个体均以差分进化算子独立进化；对于信念空间，将进化信息放入与种群空间地域对应的上层元胞网格当中，利用文化的扩散机制实现文化的进化. 实验结果表明，该算法具有收敛精度高以及全局搜索能力强等优点，在处理高维复杂优化问题时同样具有优势.

Cultural Algorithm with Cellular Space Structure

To solve the problems in the cultural algorithm has a number of problems, for example, lack of a region concept in the population space, lack of cultural evolution in the belief space, and defects such as low accuracy and being easy to fall into a local optimum. This paper proposes a new cultural algorithm based on cellular space structure. It can simulate a double-layer evolutionary system of culture algorithm by embedding a cellular space grid structure in the framework of computing population space and belief space. For the population space, the evolutionary individuals are distributed in the lower cellular space grid. The grid is divided into many areas so that individuals in each area evolve independently by using differential evolution algorithm. For the belief space, the evolution information is put into the upper grid corresponding to the population space, and the evolution of culture is realized using a diffusion mechanism of culture. Experimental results show that the algorithm is effective in convergence accuracy and global search capability, and has advantages in dealing with complex high-dimensional optimization problems.