National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China.
School of Computer and Software Engineering, Xihua University, Chengdu, China.
PLoS One. 2021 Feb 4;16(2):e0245887. doi: 10.1371/journal.pone.0245887. eCollection 2021.
In order to improve the performance of differential evolution (DE), this paper proposes a ranking-based hierarchical random mutation in differential evolution (abbreviated as RHRMDE), in which two improvements are presented. First, RHRMDE introduces a hierarchical random mutation mechanism to apply the classic "DE/rand/1" and its variant on the non-inferior and inferior group determined by the fitness value. The non-inferior group employs the traditional mutation operator "DE/rand/1" with global and random characteristics, which increases the global exploration ability and population diversity. The inferior group uses the improved mutation operator "DE/rand/1" with elite and random characteristics, which enhances the local exploitation ability and convergence speed. Second, the control parameter adaptation of RHRMDE not only considers the complexity differences of various problems but also takes individual differences into account. The proposed RHRMDE is compared with five DE variants and five non-DE algorithms on 32 universal benchmark functions, and the results show that the RHRMDE is superior over the compared algorithms.
为了提高差分进化（DE）的性能，本文提出了一种基于排序的分层随机变异差分进化（简称 RHRMDE），其中提出了两项改进。首先，RHRMDE 引入了分层随机变异机制，将经典的“DE/rand/1”及其变体应用于由适应度值确定的非劣组和劣组。非劣组采用具有全局和随机特征的传统变异算子“DE/rand/1”，从而提高了全局探索能力和种群多样性。劣组采用具有精英和随机特征的改进变异算子“DE/rand/1”，从而增强了局部开发能力和收敛速度。其次，RHRMDE 的控制参数自适应不仅考虑了各种问题的复杂度差异，还考虑了个体差异。将提出的 RHRMDE 与五种 DE 变体和五种非 DE 算法在 32 个通用基准函数上进行了比较，结果表明 RHRMDE 优于比较算法。
