Multiselection-Based Differential Evolution
针对差分进化算法在复杂多峰或平坦区域上开发不足的问题，提出多重选择策略，在三个竞赛基准上超越冠军算法，并能提升原版及其他变体的性能。
Differential evolution (DE) has been developed as a state-of-the-art population-based stochastic optimizer for continuous nonconvex search space in recent decades. It uses difference vectors among individuals to adaptively control the balance between exploration and exploitation for achieving an excellent search performance. Specifically, one-to-one selection in DE guarantees that difference vectors vary from large for exploration to small for exploitation. However, the selection may generate inappropriate scale of difference vectors, which causes insufficient exploitation when facing complex problems with, e.g., multimodal with a huge number of local optima or plain regions. In this article, we propose a multiselection-based DE (MSDE) to address such limitation. Specifically, three different types of selection strategies are adopted to deal with the different situation during a search process. The proposed MSDE is evaluated on the 2013, 2014, and 2017 IEEE congress on evolutionary computation real parameter optimization competitions. Experimental results demonstrate that the proposed MSDE significantly outperforms the winners of those competitions. We further show that, when integrating the proposed multiselection-based strategy with the original DE or other advanced DE variants, it can also improve their search performance on most of test cases. This work makes a significant contribution to advance the state of the art in the area of population-based stochastic optimizers.