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Differential Evolution Enhanced by Combining Group Learning and Elite Learning
- Authors
- Shen, Guang-Xu; Li, Jian-Yu; Sun, Pei-Fa; Jeon, Sang-Woon; Jin, Hu
- Issue Date
- Oct-2023
- Publisher
- IEEE Computer Society
- Keywords
- Differential evolution; elite learning; group learning; mutation strategy; optimization
- Citation
- 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), pp 921 - 923
- Pages
- 3
- Indexed
- SCOPUS
- Journal Title
- 2023 14th International Conference on Information and Communication Technology Convergence (ICTC)
- Start Page
- 921
- End Page
- 923
- ISSN
- 2162-1233
- Abstract
- Differential evolution (DE) is fully validated as a feasible algorithm for solving optimization problems. Additionally, for the complex optimization problems with high dimension, the traditional DE suffers from slow convergence. This paper proposes an enhanced DE algorithm that combines group learning and elite learning. The proposed algorithm improves the global search capability while guaranteeing a certain convergence speed. Through extensive experiments we confirm the superior competitiveness of the proposed DE algorithm compared to the traditional ones. © 2023 IEEE.
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Related Researcher
- Jeon, Sang Woon
- ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)