基于多级搜索区域的协同进化遗传算法*

针对传统多种群协同进化算法仍然存在收敛速度慢、计算复杂性不能随进化过程有效降低等问题，提出了一种基于多级搜索区域的协同进化遗传算法，给出了一种衡量种群进化停滞的标准。通过聚类分析将搜索区域划为三个等级，对于较高等级的区域加强搜索粒度，逐步缩小搜索范围，提高了收敛速度并降低了算法复杂度。实验结果表明，该算法是求解最优化问题的一种有效方法。

Co-evolutionary genetic algorithm based on multi-level search area

This paper proposed a co-evolutionary genetic algorithm based on multi-level search area to cope with the limitation of traditional multi-population co-evolutionary genetic algorithm, for instance, convergent rate was slow, and computational complexity could not be effectively reduced according to evolutionary process. It put forward a standard which could measure evolutionary stagnate. Divided the search spaces into three levels via clustering, and the algorithm enhanced search granularity for higher levels. As the search spaces were gradually reduced, it improved convergent speed and reduced the complexity of the algorithm. The experimental results indicate that the algorithm is an effective method for solving optimization problems.