An improved LSHADE-RSP algorithm with the Cauchy perturbation: iLSHADE-RSP

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 exploration by adopting long-tailed property Cauchy distribution, which helps algorithm generate trial vector with great diversity. Compared to previous approaches, approach perturbs target instead mutant based on jumping rate. We applied LSHADE-RSP ranked second place CEC 2018 competition single objective real-valued optimization. set 30 different and difficult problems used evaluate improved LSHADE-RSP. Our experimental results verify that significantly outperformed not only its predecessor but also several cutting-edge DE variants terms convergence speed solution accuracy.