An Improved LSHADE-RSP Algorithm with the Cauchy Perturbation: iLSHADE-RSP

TL;DR

This paper introduces an enhanced LSHADE-RSP algorithm that employs Cauchy distribution-based perturbation to improve exploration, leading to better convergence and accuracy on complex optimization problems.

Contribution

The paper proposes a novel perturbation method using Cauchy distribution applied to target vectors, significantly improving the performance of LSHADE-RSP in optimization tasks.

Findings

Outperforms previous LSHADE-RSP and other DE variants in convergence speed

Achieves higher solution accuracy on complex benchmarks

Demonstrates robustness across diverse optimization problems

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.