  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.
