A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
Recent progress in unconstrained nonlinear optimization without derivatives
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
How to solve it: modern heuristics
How to solve it: modern heuristics
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
On the Convergence of Pattern Search Algorithms
SIAM Journal on Optimization
Journal of Global Optimization
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Radial Basis Function Method for Global Optimization
Journal of Global Optimization
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Radial Basis Functions
Comparison of methods for developing dynamic reduced models for design optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions
Journal of Global Optimization
Mesh Adaptive Direct Search Algorithms for Constrained Optimization
SIAM Journal on Optimization
Improved Strategies for Radial basis Function Methods for Global Optimization
Journal of Global Optimization
Scatter search for chemical and bio-process optimization
Journal of Global Optimization
Convergence of Mesh Adaptive Direct Search to Second-Order Stationary Points
SIAM Journal on Optimization
A line up evolutionary algorithm for solving nonlinear constrained optimization problems
Computers and Operations Research
ASAGA: an adaptive surrogate-assisted genetic algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
ORBIT: Optimization by Radial Basis Function Interpolation in Trust-Regions
SIAM Journal on Scientific Computing
Improved scatter search for the global optimization of computationally expensive dynamic models
Journal of Global Optimization
Introduction to Derivative-Free Optimization
Introduction to Derivative-Free Optimization
A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions
INFORMS Journal on Computing
Scatter Search and Local NLP Solvers: A Multistart Framework for Global Optimization
INFORMS Journal on Computing
An informational approach to the global optimization of expensive-to-evaluate functions
Journal of Global Optimization
Adaptive memory programming for constrained global optimization
Computers and Operations Research
New heuristics for global optimization of complex bioprocesses
New heuristics for global optimization of complex bioprocesses
Local function approximation in evolutionary algorithms for the optimization of costly functions
IEEE Transactions on Evolutionary Computation
Surrogate modeling in the evolutionary optimization of catalytic materials
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Surrogate-assisted evolutionary programming for high dimensional constrained black-box optimization
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Computers and Operations Research
Enhancing intill sampling criteria for surrogate-based constrained optimization
Journal of Computational Methods in Sciences and Engineering - Special issue on Advances in Simulation-Driven Optimization and Modeling
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This paper presents a new algorithm for derivative-free optimization of expensive black-box objective functions subject to expensive black-box inequality constraints. The proposed algorithm, called ConstrLMSRBF, uses radial basis function (RBF) surrogate models and is an extension of the Local Metric Stochastic RBF (LMSRBF) algorithm by Regis and Shoemaker (2007a) [1] that can handle black-box inequality constraints. Previous algorithms for the optimization of expensive functions using surrogate models have mostly dealt with bound constrained problems where only the objective function is expensive, and so, the surrogate models are used to approximate the objective function only. In contrast, ConstrLMSRBF builds RBF surrogate models for the objective function and also for all the constraint functions in each iteration, and uses these RBF models to guide the selection of the next point where the objective and constraint functions will be evaluated. Computational results indicate that ConstrLMSRBF is better than alternative methods on 9 out of 14 test problems and on the MOPTA08 problem from the automotive industry (Jones, 2008 [2]). The MOPTA08 problem has 124 decision variables and 68 inequality constraints and is considered a large-scale problem in the area of expensive black-box optimization. The alternative methods include a Mesh Adaptive Direct Search (MADS) algorithm (Abramson and Audet, 2006 [3]; Audet and Dennis, 2006 [4]) that uses a kriging-based surrogate model, the Multistart LMSRBF algorithm by Regis and Shoemaker (2007a) [1] modified to handle black-box constraints via a penalty approach, a genetic algorithm, a pattern search algorithm, a sequential quadratic programming algorithm, and COBYLA (Powell, 1994 [5]), which is a derivative-free trust-region algorithm. Based on the results of this study, the results in Jones (2008) [2] and other approaches presented at the ISMP 2009 conference, ConstrLMSRBF appears to be among the best, if not the best, known algorithm for the MOPTA08 problem in the sense of providing the most improvement from an initial feasible solution within a very limited number of objective and constraint function evaluations.