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Solving large-scale many-objective optimization problems by covariance matrix adaptation evolution strategy with scalable small subpopulations.
- Source :
-
Information Sciences . Jan2020, Vol. 509, p457-469. 13p. - Publication Year :
- 2020
-
Abstract
- Despite the recent development in evolutionary multi- and many-objective optimization, the problems with large-scale decision variables still remain challenging. In this work, we propose a scalable small subpopulations based covariance matrix adaptation evolution strategy, namely S3-CMA-ES, for solving many-objective optimization problems with large-scale decision variables. The proposed S3-CMA-ES attempts to approximate the set of Pareto-optimal solutions using a series of small subpopulations instead of a whole population, where each subpopulation converges to only one solution. In the proposed S3-CMA-ES, a diversity improvement strategy is designed to generate and select new solutions. The performance of S3-CMA-ES is compared with five representative algorithms on 36 test instances with 5–15 objectives and 500–1500 decision variables. The empirical results demonstrate the superiority of the proposed S3-CMA-ES. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 509
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 139031265
- Full Text :
- https://doi.org/10.1016/j.ins.2018.10.007