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Dual Elite Groups-Guided Differential Evolution for Global Numerical Optimization by Tian-Tian Wang; Qiang Yang; Xu-Dong Gao is a Mathematics article available to read on EtoBox.
What is Dual Elite Groups-Guided Differential Evolution for Global Numerical Optimization about?
Differential evolution (DE) has shown remarkable performance in solving continuous optimization problems. However, its optimization performance still encounters limitations when confronted with complex optimization problems with lots of local regions. To address this issue, this paper proposes a dual elite groups-guided mutation strategy called “DE/current-to-duelite/1” for DE. As a result, a novel DE variant called DEGGDE is developed. Instead of only using the elites in the current population to direct the evolution of all individuals, DEGGDE additionally maintains an archive to store the obsolete parent individuals and then assembles the elites in both the current population and the archive to guide the mutation of all individuals. In this way, the diversity of the guiding exemplars in the mutation is expectedly promoted. With the guidance of these diverse elites, a good balance between exploration of the complex search space and exploitation of the found promising regions is hopefully maintained in DEGGDE. As a result, DEGGDE expectedly achieves good optimization performance in solving complex optimization problems. A large number of experiments are conducted on the CEC’2017 be
Who reads Dual Elite Groups-Guided Differential Evolution for Global Numerical Optimization?
It is typically read by researchers, students, and practitioners in Mathematics.
- Author
- Tian-Tian Wang; Qiang Yang; Xu-Dong Gao
- Publisher
- MDPI AG
- Published
- 2023
- Language
- EN
- Field
- Mathematics (Physical Sciences)