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

arXiv: https://arxiv.org/abs/2006.02591 · HTML: https://mlsanity.com/paper/2006.02591/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [RDEx-CSOP: Feasibility-Aware Reconstructed Differential Evolution with Adaptive epsilon-Constraint Ranking](https://mlsanity.com/paper/2603.27090/) (2026)
- [An Efficient Reconstructed Differential Evolution Variant by Some of the Current State-of-the-art Strategies for Solving Single Objective Bound Constrained Problems](https://mlsanity.com/paper/2404.16280/) (2024)
- [RDEx-SOP: Exploitation-Biased Reconstructed Differential Evolution for Fixed-Budget Bound-Constrained Single-Objective Optimization](https://mlsanity.com/paper/2603.27089/) (2026)
- [RDEx-MOP: Indicator-Guided Reconstructed Differential Evolution for Fixed-Budget Multiobjective Optimization](https://mlsanity.com/paper/2603.27092/) (2026)

## Abstract (arXiv)

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.