The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model
Abstract
In some expensive multiobjective optimization problems (MOPs), several function evaluations can be carried out in a batch way. Therefore, it is very desirable to develop methods which can generate multipler test points simultaneously. This paper proposes such a method, called MOEA/D-EGO, for dealing with expensive multiobjective optimization. MOEA/D-EGO decomposes an MOP in question into a number of single-objective optimization subproblems. A predictive model is built for each subproblem based on the points evaluated so far. Effort has been made to reduce the overhead for modeling and to improve the prediction quality. At each generation, MOEA/D is used for maximizing the expected improvement metric values of all the subproblems, and then several test points are selected for evaluation. Extensive experimental studies have been carried out to investigate the ability of the proposed algorithm.
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Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Zhang, Q., & Li, H. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.892759 · Cited by 10,069
Self-citationPattern Recognition with Fuzzy Objective Function Algorithms
Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881
Efficient Global Optimization of Expensive Black-Box Functions
Jones, D. R., Schonlau, M., & Welch, W. J. (1998) · Journal of Global Optimization · doi:10.1023/a:1008306431147 · Cited by 8,070
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 7,415
Design and Analysis of Computer Experiments
Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989) · Statistical Science · doi:10.1214/ss/1177012413 · Cited by 6,919
Gaussian Processes in Machine Learning
Rasmussen, C. E. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-28650-9_4 · Cited by 5,397
Empirical Model Building and Response Surfaces
Freeny, A. (1988) · Technometrics · doi:10.1080/00401706.1988.10488371 · Cited by 5,235
Nonlinear Multiobjective Optimization
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Cited by
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
Zhang, Q., Liu, W., & Li, H. (2009) · 2009 IEEE Congress on Evolutionary Computation · doi:10.1109/cec.2009.4982949 · Cited by 624
Self-citationMulti-objective Bayesian optimization using Pareto-frontier entropy
Suzuki, S., Takeno, S., Tamura, T., Shitara, K., & Karasuyama, M. (2019) · arXiv · doi:10.48550/arxiv.1906.00127 · Cited by 40
Song, Z., Wang, H., & Jin, Y. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3281822 · Cited by 38
Metadata
- Authors
- Zhang, Q., Liu, W., Tsang, E., & Virginas, B.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2010-06
- Volume
- 14
- Issue
- 3
- Pages
- 456-474
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W2105245738
- Cited by
- 746
- References
- 53
- FWCI
- 12.55
- Type
- journal article
- Language
- en
- Open access
- closed
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