Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization Using Radial Basis Functions
This paper develops a surrogate-assisted evolutionary programming (EP) algorithm for constrained expensive black-box optimization that can be used for high-dimensional problems with many black-box inequality constraints. The proposed method does not use a penalty function and it builds surrogates fo...
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| Published in: | IEEE transactions on evolutionary computation Vol. 18; no. 3; pp. 326 - 347 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York, NY
IEEE
01.06.2014
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1089-778X, 1941-0026 |
| Online Access: | Get full text |
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| Abstract | This paper develops a surrogate-assisted evolutionary programming (EP) algorithm for constrained expensive black-box optimization that can be used for high-dimensional problems with many black-box inequality constraints. The proposed method does not use a penalty function and it builds surrogates for the objective and constraint functions. Each parent generates a large number of trial offspring in each generation. Then, the surrogate functions are used to identify the trial offspring that are predicted to be feasible with the best predicted objective function values or those with the minimum number of predicted constraint violations. The objective and constraint functions are then evaluated only on the most promising trial offspring from each parent, and the method proceeds in the same way as in a standard EP. In the numerical experiments, the type of surrogate used to model the objective and each of the constraint functions is a cubic radial basis function (RBF) augmented by a linear polynomial. The resulting RBF-assisted EP is applied to 18 benchmark problems and to an automotive problem with 124 decision variables and 68 black-box inequality constraints. The proposed method is much better than a traditional EP, a surrogate-assisted penalty-based EP, stochastic ranking evolution strategy, scatter search, and CMODE, and it is competitive with ConstrLMSRBF on the problems used. |
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| AbstractList | This paper develops a surrogate-assisted evolutionary programming (EP) algorithm for constrained expensive black-box optimization that can be used for high-dimensional problems with many black-box inequality constraints. The proposed method does not use a penalty function and it builds surrogates for the objective and constraint functions. Each parent generates a large number of trial offspring in each generation. Then, the surrogate functions are used to identify the trial offspring that are predicted to be feasible with the best predicted objective function values or those with the minimum number of predicted constraint violations. The objective and constraint functions are then evaluated only on the most promising trial offspring from each parent, and the method proceeds in the same way as in a standard EP. In the numerical experiments, the type of surrogate used to model the objective and each of the constraint functions is a cubic radial basis function (RBF) augmented by a linear polynomial. The resulting RBF-assisted EP is applied to 18 benchmark problems and to an automotive problem with 124 decision variables and 68 black-box inequality constraints. The proposed method is much better than a traditional EP, a surrogate-assisted penalty-based EP, stochastic ranking evolution strategy, scatter search, and CMODE, and it is competitive with ConstrLMSRBF on the problems used. |
| Author | Regis, Rommel G. |
| Author_xml | – sequence: 1 givenname: Rommel G. surname: Regis fullname: Regis, Rommel G. email: rregis@sju.edu organization: Department of Mathematics, Saint Joseph's University, Philadelphia, PA, USA |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28562577$$DView record in Pascal Francis |
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| Keywords | surrogate-assisted evolutionary algorithms high-dimensional optimization evolutionary programming radial basis functions constrained optimization Black-box optimization Motor car Search strategy Probabilistic approach Hierarchical classification Decision making Evolutionary algorithm Black box Constrained optimization Radial basis function Penalty function Assisted programming Value function Multidimensional analysis Inequality constraint Cubics Objective function Metamodel |
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| SubjectTerms | Adaptation models Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Black-box optimization Computational modeling Computer science; control theory; systems Constrained Optimization Constraints Construction Evolutionary algorithms Evolutionary computation Evolutionary programming Exact sciences and technology Genetic algorithms Highdimensional optimization Mathematical analysis Mathematical models Optimization Parents Programming Radial basis function Radial basis functions Simulation Software Surrogateassisted evolutionary algorithms Theoretical computing |
| Title | Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization Using Radial Basis Functions |
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