The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Covariance Matrix Adaptation for Multi-objective Optimization
Abstract
The covariancematrix adaptation evolution strategy (CMA-ES) is one of themost powerful evolutionary algorithms for real-valued single-objective optimization. In this paper, we develop a variant of the CMA-ES for multi-objective optimization (MOO). We first introduce a single-objective, elitist CMA-ES using plus-selection and step size control based on a success rule. This algorithm is compared to the standard CMA-ES. The elitist CMA-ES turns out to be slightly faster on unimodal functions, but is more prone to getting stuck in sub-optimal local minima. In the new multi-objective CMAES (MO-CMA-ES) a population of individuals that adapt their search strategy as in the elitist CMA-ES is maintained. These are subject to multi-objective selection. The selection is based on non-dominated sorting using either the crowding-distance or the contributing hypervolume as second sorting criterion. Both the elitist single-objective CMA-ES and the MO-CMA-ES inherit important invariance properties, in particular invariance against rotation of the search space, from the original CMA-ES. The benefits of the new MO-CMA-ES in comparison to the well-known NSGA-II and to NSDE, a multi-objective differential evolution algorithm, are experimentally shown.
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Cites
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(2007) · Genetic and Evolutionary Computation Series · doi:10.1007/978-0-387-36797-2 · Cited by 5,686
Completely Derandomized Self-Adaptation in Evolution Strategies
Hansen, N., & Ostermeier, A. (2001) · Evolutionary Computation · doi:10.1162/106365601750190398 · Cited by 4,397
Self-citationPerformance assessment of multiobjective optimizers: an analysis and review
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Cited by
Platemo: a matlab platform for evolutionary multi-objective optimization [educational forum]
Tian, Y., Cheng, R., Zhang, X., & Jin, Y. (2017) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2017.2742868 · Cited by 2,777
A Survey on the Hypervolume Indicator in Evolutionary Multiobjective Optimization
Shang, K., Ishibuchi, H., He, L., & Pang, L. M. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2020.3013290 · Cited by 380
Review of numerical optimization techniques for meta-device design [Invited]
Campbell, S. D., Sell, D., Jenkins, R. P., Whiting, E. B., Fan, J. A., & Werner, D. H. (2019) · Optical Materials Express · doi:10.1364/ome.9.001842 · Cited by 322
Meta-atom library generation via an efficient multi-objective shape optimization method
Whiting, E. B., Campbell, S. D., Kang, L., & Werner, D. H. (2020) · Optics Express · doi:10.1364/oe.398332 · Cited by 67
Metadata
- Authors
- Igel, C., Hansen, N., & Roth, S.
- Venue
- Evolutionary Computation
- Publisher
- MIT Press - Journals
- Published
- 2007-03
- Volume
- 15
- Issue
- 1
- Pages
- 1-28
- ISSN
- 1063-6560 (ISSN-L)
- 1530-9304
- OpenAlex
- W2160088187
- Cited by
- 788
- References
- 33
- FWCI
- 33.11
- Type
- journal article
- Language
- en
- Open access
- closed
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