Differential evolution (DE) has been recognized as one of the most popular evolutionary algorithms. There are numerous DE variants adopting multi-operators based cooperation strategy to improve their performance, but almost all of the adopted cooperation strategies are essentially implemented at the individual level or population level, and the implementation at the dimension level are scarce. In this paper, an adaptive dimension level adjustment (ADLA) framework is designed to relieve the premature convergence or stagnation problem faced by DE algorithm, which can be easily combined with diverse DE variants. When the current optimal individual cannot get improved for a given uninterrupted iterations, ADLA framework will be triggered to select some individuals at random according to specific rule and reinitialize portion of their dimensions from a dynamic search space that adjusted by a population level macroparameter and one individual level microparameter. Moreover, ADLA framework contains two reinitialization operators with different search characteristics, and the coordination between them is executed at the dimension level, which has potential advantages in balancing the global exploration ability and local exploitation ability. Extensive comparison experiments are carried out based on IEEE CEC 2014 test platform, two basic DE algorithms and six outstanding DE variants. The experimental results demonstrate that ADLA framework can memorably enhance the performance of every DE algorithm used for comparison.

差分进化（DE）已被公认为最流行的进化算法之一。有许多DE变体采用基于多操作者的合作策略来提高其绩效，但是几乎所有采用的合作策略基本上都是在个人级别或人口级别上实施的，而在维度级别上的实施则很少。本文设计了一种自适应维数水平调整（ADLA）框架，以缓解DE算法面临的过早收敛或停滞问题，该算法可以轻松地与各种DE变体组合。如果当前的最佳个体无法在给定的不间断迭代中得到改善，将触发ADLA框架，根据特定规则随机选择一些个体，然后从动态搜索空间重新初始化其维度的一部分，该动态搜索空间由总体水平的宏观参数和一个个体水平的微观参数调整。此外，ADLA框架包含两个具有不同搜索特性的重新初始化运算符，并且它们之间的协调是在维级别上执行的，这在平衡全局勘探能力和局部开发能力方面具有潜在的优势。基于IEEE CEC 2014测试平台，两种基本的DE算法和六个出色的DE变体进行了广泛的比较实验。实验结果表明，ADLA框架可以显着提高用于比较的每种DE算法的性能。