IEEE Trans Cybern. 2013 Dec;43(6):2066-81. doi: 10.1109/TCYB.2013.2239988.
Differential evolution (DE) has been proven to be one of the most powerful global numerical optimization algorithms in the evolutionary algorithm family. The core operator of DE is the differential mutation operator. Generally, the parents in the mutation operator are randomly chosen from the current population. In nature, good species always contain good information, and hence, they have more chance to be utilized to guide other species. Inspired by this phenomenon, in this paper, we propose the ranking-based mutation operators for the DE algorithm, where some of the parents in the mutation operators are proportionally selected according to their rankings in the current population. The higher ranking a parent obtains, the more opportunity it will be selected. In order to evaluate the influence of our proposed ranking-based mutation operators on DE, our approach is compared with the jDE algorithm, which is a highly competitive DE variant with self-adaptive parameters, with different mutation operators. In addition, the proposed ranking-based mutation operators are also integrated into other advanced DE variants to verify the effect on them. Experimental results indicate that our proposed ranking-based mutation operators are able to enhance the performance of the original DE algorithm and the advanced DE algorithms.
差分进化 (DE) 已被证明是进化算法家族中最强大的全局数值优化算法之一。DE 的核心算子是差分变异算子。通常，变异算子中的父代是从当前种群中随机选择的。在自然界中，优良的物种总是包含良好的信息，因此，它们更有机会被用来指导其他物种。受此现象启发，本文提出了用于 DE 算法的基于排序的变异算子，其中变异算子中的一些父代是根据它们在当前种群中的排名按比例选择的。父代的排名越高，被选择的机会就越多。为了评估我们提出的基于排序的变异算子对 DE 的影响，我们将我们的方法与 jDE 算法进行了比较，jDE 算法是一种具有自适应参数的高度竞争的 DE 变体，具有不同的变异算子。此外，我们还将基于排序的变异算子集成到其他先进的 DE 变体中，以验证它们对这些变体的影响。实验结果表明，我们提出的基于排序的变异算子能够增强原始 DE 算法和先进的 DE 算法的性能。
