Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model
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...
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| Veröffentlicht in: | IEEE transactions on evolutionary computation 2010-06, Vol.14 (3), p.456-474 |
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| container_end_page | 474 |
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| container_title | IEEE transactions on evolutionary computation |
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| creator | Qingfu Zhang Wudong Liu Tsang, Edward Virginas, Botond |
| description | 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. |
| doi_str_mv | 10.1109/TEVC.2009.2033671 |
| format | Article |
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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. 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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. 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| source | IEEE Electronic Library (IEL) |
| subjects | Acoustic testing Algorithms Applied sciences Artificial intelligence Computational efficiency Computer science control theory systems Computer simulation Dealing Decomposition Design optimization Evolutionary algorithm Evolutionary algorithms Exact sciences and technology expensive optimization Gaussian Gaussian processes Gaussian stochastic processes Learning and adaptive systems Mathematical analysis Mathematical models multiobjective optimization Optimization Optimization methods Pareto optimality Pareto optimization Physics computing Predictive models Stochastic processes |
| title | Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model |
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