Keyword

surrogate-based optimization

Description: An optimization method that uses an approximate model of a system to guide the search for improved solutions with fewer costly evaluations.

Primary topic: Advanced Multi-Objective Optimization Algorithms (34%)

OpenAlex ID: keywords/surrogate-based-optimization

Recent advances and applications of machine learning in solid-state materials science

2019 · Jonathan Schmidt, Mário R. G. Marques, et al. · npj Computational Materials · 2,619

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Recent advances in surrogate-based optimization

2009 · Alexander I.J. Forrester, Andy J. Keane · Progress in Aerospace Sciences · 2,369

A Taxonomy of Global Optimization Methods Based on Response Surfaces

2001 · Donald R. Jones · Journal of Global Optimization · 2,160

Engineering Design via Surrogate Modelling: A Practical Guide

2008 · Andy Keane, Alexander Forrester, et al. · American Institute of Aeronautics and Astronautics eBooks · 1,685

ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems

2006 · J. Knowles · IEEE Transactions on Evolutionary Computation · 1,257

DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's manual.

2010 · Michael Eldred, Keith Dalbey, et al. · 775

Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels

2006 · M.T.M. Emmerich, K.C. Giannakoglou, et al. · IEEE Transactions on Evolutionary Computation · 761

Advances in surrogate based modeling, feasibility analysis, and optimization: A review

2017 · Atharv Bhosekar, Marianthi Ierapetritou · Computers & Chemical Engineering · 711

Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions

2009 · Songqing Shan, G. Gary Wang · Structural and Multidisciplinary Optimization · 598

Efficient Optimization Design Method Using Kriging Model

2005 · Shinkyu Jeong, Mitsuhiro Murayama, et al. · Journal of Aircraft · 535

Derivative-free optimization methods

2019 · Jeffrey Larson, Matt Menickelly, et al. · Acta Numerica · 454

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Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

2017 · Chaoli Sun, Yaochu Jin, et al. · IEEE Transactions on Evolutionary Computation · 428

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems

2017 · Handing Wang, Yaochu Jin, et al. · IEEE Transactions on Cybernetics · 415

A comparison of approximation modeling techniques - Polynomial versus interpolating models

1998 · Anthony Giunta, Layne Watson · AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization (MA&O) · 402

Overview of Modern Design of Experiments Methods for Computational Simulations (Invited)

2003 · Anthony Giunta, Steven Wojtkiewicz, et al. · AIAA Aerospace Sciences Meeting and Exhibit · 393

Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization

2020 · Xinshuai Zhang, Fangfang Xie, et al. · Computer Methods in Applied Mechanics and Engineering · 381

Recent advances and applications of surrogate models for finite element method computations: a review

2022 · Jakub Kudela, Radomil Matousek · Soft Computing · 375

Accelerating Evolutionary Algorithms With Gaussian Process Fitness Function Models

2005 · D. Buche, N.N. Schraudolph, et al. · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · 335

A benchmark of kriging-based infill criteria for noisy optimization

2013 · Victor Picheny, Tobias Wagner, et al. · Structural and Multidisciplinary Optimization · 327

Surrogate-assisted hierarchical particle swarm optimization

2018 · Haibo Yu, Ying Tan, et al. · Information Sciences · 313

A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses

2017 · Raul Yondo, Esther Andrés, et al. · Progress in Aerospace Sciences · 311

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

2017 · Tinkle Chugh, Karthik Sindhya, et al. · Soft Computing · 306

A statistical method for global optimization

2003 · D.D. Cox, S. John · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · 302

An optimization methodology of alkaline–surfactant–polymer flooding processes using field scale numerical simulation and multiple surrogates

2005 · Luis E. Zerpa, Nestor V. Queipo, et al. · Journal of Petroleum Science and Engineering · 301

Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor

2019 · Yanan Sun, Handing Wang, et al. · IEEE Transactions on Evolutionary Computation · 291

Works count: 5,800

Citation count: 80,450