An Improved LSHADE-RSP Algorithm with the Cauchy Perturbation: iLSHADE-RSP
Abstract
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.
Cite
@article{arxiv.2006.02591,
title = {An Improved LSHADE-RSP Algorithm with the Cauchy Perturbation: iLSHADE-RSP},
author = {Tae Jong Choi and Chang Wook Ahn},
journal= {arXiv preprint arXiv:2006.02591},
year = {2020}
}