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A resource allocation-based multi-objective evolutionary algorithm for large-scale multi-objective optimization.
- Source :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications; Dec2023, Vol. 27 Issue 23, p17809-17831, 23p
- Publication Year :
- 2023
-
Abstract
- In large-scale multi-objective optimization problems (LSMOPs), multiple conflicting objectives and hundreds even thousands of decision variables are contained. Therefore, it is a great challenge to address LSMOPs due to the curse of dimensionality. To tackle LSMOPs, this paper proposes a resource allocation-based multi-objective optimization evolutionary algorithm. In the proposed algorithm, decision variables are firstly divided into convergence-related variables and diversity-related variables by the proposed layer thickness-based variable classification (LTVC) method. Then, a resource allocation-based convergence optimization strategy is introduced for the convergence-related variables, which can allocate more computational resource to the sub-component with the best contribution. For the diversity-related variables, diversity optimization technique is adopted. Finally, the experimental results verify that the proposed algorithm has a competitive performance compared with some state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]
- Subjects :
- EVOLUTIONARY algorithms
MATHEMATICAL optimization
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 14327643
- Volume :
- 27
- Issue :
- 23
- Database :
- Complementary Index
- Journal :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 172972026
- Full Text :
- https://doi.org/10.1007/s00500-023-09061-4