Genetic algorithm for non-linear mixed integer programming problems and its applications
Computers and Industrial Engineering
Computers and Operations Research
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Pattern Search Algorithms for Mixed Variable Programming
SIAM Journal on 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 Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
MINLPLib--A Collection of Test Models for Mixed-Integer Nonlinear Programming
INFORMS Journal on Computing
Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions
Journal of Global Optimization
Relaxation and Decomposition Methods for Mixed Integer Nonlinear Programming (International Series of Numerical Mathematics)
Improved Strategies for Radial basis Function Methods for Global Optimization
Journal of Global Optimization
Convergence of Mesh Adaptive Direct Search to Second-Order Stationary Points
SIAM Journal on Optimization
Nonsmooth optimization through Mesh Adaptive Direct Search and Variable Neighborhood Search
Journal of Global Optimization
An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization
Journal of Global Optimization
Weight minimization of trusses with genetic algorithm
Applied Soft Computing
Mixture surrogate models based on Dempster-Shafer theory for global optimization problems
Journal of Global Optimization
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
An algorithmic framework for convex mixed integer nonlinear programs
Discrete Optimization
Derivative-free methods for bound constrained mixed-integer optimization
Computational Optimization and Applications
Flicker: a dynamically adaptive architecture for power limited multicore systems
Proceedings of the 40th Annual International Symposium on Computer Architecture
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This paper introduces a surrogate model based algorithm for computationally expensive mixed-integer black-box global optimization problems with both binary and non-binary integer variables that may have computationally expensive constraints. The goal is to find accurate solutions with relatively few function evaluations. A radial basis function surrogate model (response surface) is used to select candidates for integer and continuous decision variable points at which the computationally expensive objective and constraint functions are to be evaluated. In every iteration multiple new points are selected based on different methods, and the function evaluations are done in parallel. The algorithm converges to the global optimum almost surely. The performance of this new algorithm, SO-MI, is compared to a branch and bound algorithm for nonlinear problems, a genetic algorithm, and the NOMAD (Nonsmooth Optimization by Mesh Adaptive Direct Search) algorithm for mixed-integer problems on 16 test problems from the literature (constrained, unconstrained, unimodal and multimodal problems), as well as on two application problems arising from structural optimization, and three application problems from optimal reliability design. The numerical experiments show that SO-MI reaches significantly better results than the other algorithms when the number of function evaluations is very restricted (200-300 evaluations).