A review of surrogate-assisted evolutionary algorithms for expensive optimization problems
C He, Y Zhang, D Gong, X Ji - Expert Systems with Applications, 2023 - Elsevier
Many problems in real life can be seen as Expensive Optimization Problems (EOPs).
Compared with traditional optimization problems, the evaluation cost of candidate solutions …
Compared with traditional optimization problems, the evaluation cost of candidate solutions …
A novel evolutionary sampling assisted optimization method for high-dimensional expensive problems
X Wang, GG Wang, B Song, P Wang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Surrogate-assisted evolutionary algorithms (SAEAs) are promising methods for solving high-
dimensional expensive problems. The basic idea of SAEAs is the integration of nature …
dimensional expensive problems. The basic idea of SAEAs is the integration of nature …
A sequential constraints updating approach for Kriging surrogate model-assisted engineering optimization design problem
Kriging surrogate model has been widely used in engineering design optimization problems
to replace computational cost simulations. To facilitate the usage of the Kriging surrogate …
to replace computational cost simulations. To facilitate the usage of the Kriging surrogate …
Efficient hierarchical surrogate-assisted differential evolution for high-dimensional expensive optimization
Surrogate-assisted evolutionary algorithms have gained increasingly attention due to the
promising search capabilities for solving computationally expensive optimization problems …
promising search capabilities for solving computationally expensive optimization problems …
A surrogate-assisted two-stage differential evolution for expensive constrained optimization
Surrogate-assisted evolutionary algorithms (SAEAs) have been successfully employed for
expensive optimization. However, most SAEAs are designed for expensive unconstrained …
expensive optimization. However, most SAEAs are designed for expensive unconstrained …
Surrogate-assisted grey wolf optimization for high-dimensional, computationally expensive black-box problems
In this paper, a Surrogate-Assisted Grey Wolf Optimization (SAGWO) algorithm for high-
dimensional and computationally expensive problems is presented, where Radial Basis …
dimensional and computationally expensive problems is presented, where Radial Basis …
Kriging-assisted teaching-learning-based optimization (KTLBO) to solve computationally expensive constrained problems
H Dong, P Wang, C Fu, B Song - Information Sciences, 2021 - Elsevier
In this paper, a novel algorithm KTLBO is presented to achieve computationally expensive
constrained optimization. In KTLBO, Kriging is adopted to develop dynamically updated …
constrained optimization. In KTLBO, Kriging is adopted to develop dynamically updated …
A fast surrogate-assisted particle swarm optimization algorithm for computationally expensive problems
Although many surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to
solve computationally expensive problems, they usually need to consume plenty of …
solve computationally expensive problems, they usually need to consume plenty of …
Surrogate-assisted teaching-learning-based optimization for high-dimensional and computationally expensive problems
H Dong, P Wang, X Yu, B Song - Applied Soft Computing, 2021 - Elsevier
In this work, a surrogate-assisted teaching-learning-based optimization algorithm is
presented for high-dimensional and computationally expensive black-box optimization …
presented for high-dimensional and computationally expensive black-box optimization …
An efficient constrained global optimization algorithm with a clustering-assisted multiobjective infill criterion using Gaussian process regression for expensive …
P Jiang, Y Cheng, J Yi, J Liu - Information Sciences, 2021 - Elsevier
Constrained optimization problems trouble engineers and researchers because of their high
complexity and computational cost. When the objective function and constraints are both …
complexity and computational cost. When the objective function and constraints are both …