An Improved LSHADE-RSP Algorithm with the Cauchy Perturbation: iLSHADE-RSP
This work addresses optimization challenges in evolutionary algorithms for researchers and practitioners, representing an incremental improvement over existing methods.
The paper tackles improving optimization performance in differential evolution by using a Cauchy distribution to perturb target vectors, enhancing exploration and diversity. The improved algorithm, iLSHADE-RSP, significantly outperformed its predecessor and other state-of-the-art variants in convergence speed and solution accuracy on 30 difficult optimization problems from the CEC 2018 competition.
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.