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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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 18; no. 3; pp. 326 - 347
Main Author: Regis, Rommel G.
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:
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
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
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary: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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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2013.2262111