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An improved LSHADE-RSP algorithm with the Cauchy perturbation: iLSHADE-RSP.
- Source :
-
Knowledge-Based Systems . Mar2021, Vol. 215, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
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 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09507051
- Volume :
- 215
- Database :
- Academic Search Index
- Journal :
- Knowledge-Based Systems
- Publication Type :
- Academic Journal
- Accession number :
- 148633214
- Full Text :
- https://doi.org/10.1016/j.knosys.2020.106628