SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems
From MaRDI portal
Publication:5131689
DOI10.1287/ijoc.2017.0749OpenAlexW2740683467MaRDI QIDQ5131689
Publication date: 9 November 2020
Published in: INFORMS Journal on Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1287/ijoc.2017.0749
multiobjective optimizationradial basis functionsurrogate modelsblack-box optimizationcomputationally expensive optimization
Related Items (9)
An algorithmic framework for the optimization of computationally expensive bi-fidelity black-box problems ⋮ Integrating \(\varepsilon \)-dominance and RBF surrogate optimization for solving computationally expensive many-objective optimization problems ⋮ Decomposition and Coordination for Many-Objective Optimization ⋮ Non-intrusive model order reduction for parametric radiation transport simulations ⋮ Surrogate-Based Promising Area Search for Lipschitz Continuous Simulation Optimization ⋮ Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints ⋮ Multi-Objective Evolutionary Algorithms: Past, Present, and Future ⋮ DMulti-MADS: mesh adaptive direct multisearch for bound-constrained blackbox multiobjective optimization ⋮ Surrogate optimization of deep neural networks for groundwater predictions
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Surrogate-assisted multiobjective evolutionary algorithms for structural shape and sizing optimisation
- Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for~computationally expensive black-box global optimization problems
- SO-MI: a surrogate model algorithm for computationally expensive nonlinear mixed-integer black-box global optimization problems
- Mixture surrogate models based on Dempster-Shafer theory for global optimization problems
- SO-I: a surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications
- Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection
- Multiobjective optimization. Interactive and evolutionary approaches
- Multivariate adaptive regression splines
- Multiple objective decision making - methods and applications. A state- of-the-art survey. In collaboration with Sudhakar R. Paidy and Kwangsun Yoon
- Efficient global optimization of expensive black-box functions
- Nonlinear multiobjective optimization
- A taxonomy of global optimization methods based on response surfaces
- MISO: mixed-integer surrogate optimization framework
- Global Convergence of Radial Basis Function Trust-Region Algorithms for Derivative-Free Optimization
- A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions
- Introduction to Derivative-Free Optimization
- ORBIT: Optimization by Radial Basis Function Interpolation in Trust-Regions
- Multicriteria optimization using a genetic algorithm for determining a Pareto set
- Surrogate-Based Modeling and Optimization
- A Derivative-Free Trust-Region Method for Biobjective Optimization
- Multicriteria Optimization
- Evolutionary Algorithms for Solving Multi-Objective Problems
- A radial basis function method for global optimization
This page was built for publication: SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems
