论文标题
带有Cauchy扰动的改进的Lshade-RSP算法：ILSHADE-RSP
An Improved LSHADE-RSP Algorithm with the Cauchy Perturbation: iLSHADE-RSP
论文作者
论文摘要
本文提出了一种提高最先进差异演化（DE）变体优化性能的新方法。该技术可以通过采用凯奇分布的长尾属性来增加探索，这有助于算法产生具有巨大多样性的试验向量。与以前的方法相比，提出的方法基于跳跃速率，而不是突变矢量。我们将提出的方法应用于CEC 2018竞赛中的Lshade-RSP在单一目标实价优化方面排名第二。一组30种不同且困难的优化问题用于评估改进的Lshade-RSP的优化性能。我们的实验结果验证了改进的Lshade-RSP不仅胜过其前身LSHADE-RSP，而且在收敛速度和溶液的准确性方面都胜过了几种尖端的DE变体。
A new method for improving the optimization performance of a state-of-the-art differential evolution (DE) variant is proposed in this paper. The technique can increase the exploration by adopting the long-tailed property of the Cauchy distribution, which helps the algorithm to generate a trial vector with great diversity. Compared to the previous approaches, the proposed approach perturbs a target vector instead of a mutant vector based on a jumping rate. We applied the proposed approach to LSHADE-RSP ranked second place in the CEC 2018 competition on single objective real-valued optimization. A set of 30 different and difficult optimization problems is used to evaluate the optimization performance of the improved LSHADE-RSP. Our experimental results verify that the improved LSHADE-RSP significantly outperformed not only its predecessor LSHADE-RSP but also several cutting-edge DE variants in terms of convergence speed and solution accuracy.
