期刊
KNOWLEDGE-BASED SYSTEMS
卷 215, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2020.106628
关键词
Artificial intelligence; Evolutionary algorithm; Differential evolution; Mathematical optimization
资金
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [2020R1A2C1103138]
- National Research Foundation of Korea [2020R1A2C1103138] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper proposes a new method to improve the optimization performance of a state-of-the-art DE variant by utilizing the long-tailed property of the Cauchy distribution. Experimental results demonstrate that the improved DE variant significantly outperforms its predecessor and several cutting-edge DE variants in terms of convergence speed and solution accuracy.
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 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. (C) 2020 Elsevier B.V. All rights reserved.
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