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

• URL: http://arxiv.org/abs/2006.02591v1
• Date: Thu, 4 Jun 2020 00:03:34 GMT
• Title: An Improved LSHADE-RSP Algorithm with the Cauchy Perturbation: iLSHADE-RSP
• Authors: Tae Jong Choi and Chang Wook Ahn
• Abstract summary: The technique can increase the exploration by adopting the long-tailed property of the Cauchy distribution. Compared to the previous approaches, the proposed approach perturbs a target vector instead of a mutant vector based on a jumping rate. A set of 30 different and difficult optimization problems is used to evaluate the optimization performance of the improved LSHADE-RSP.
• Score: 9.777183117452235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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