A Surrogate-Assisted Differential Evolution with fitness-independent parameter adaptation for high-dimensional expensive optimization
L Yu, C Ren, Z Meng - Information Sciences, 2024 - Elsevier
Surrogate-assisted evolutionary algorithms (SAEAs) have gained considerable attention
owing to their ability of tackling expensive optimization problems (EOPs). The surrogate …
owing to their ability of tackling expensive optimization problems (EOPs). The surrogate …
Adaptive multi-surrogate and module-based optimization algorithm for high-dimensional and computationally expensive problems
M Wu, J Xu, L Wang, C Zhang, H Tang - Information Sciences, 2023 - Elsevier
Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for solving
computationally expensive optimization problems. However, the performance of SAEAs …
computationally expensive optimization problems. However, the performance of SAEAs …
A surrogate-assisted constrained optimization evolutionary algorithm by searching multiple kinds of global and local regions
Y Zeng, Y Cheng, J Liu - IEEE Transactions on Evolutionary …, 2023 - ieeexplore.ieee.org
This paper proposes a surrogate-assisted evolutionary algorithm to tackle expensive
inequality-constrained optimization problems through global exploration and local …
inequality-constrained optimization problems through global exploration and local …
Classification model-based and assisted environment selection for evolutionary algorithms to solve high-dimensional expensive problems
Surrogate-assisted evolutionary algorithms (SAEAs) have been proven to be very effective
in tackling low-dimensional expensive problems. However, it remains a challenge to solve …
in tackling low-dimensional expensive problems. However, it remains a challenge to solve …
Multi-fidelity global optimization using a data-mining strategy for computationally intensive black-box problems
In this paper, a new Multi-Fidelity Global Optimization algorithm using a data-mining strategy
named MFGO is presented to solve computationally intensive black-box problems, where …
named MFGO is presented to solve computationally intensive black-box problems, where …
A data-driven evolutionary algorithm with multi-evolutionary sampling strategy for expensive optimization
Surrogate-assisted evolutionary algorithms, which combine the powerful searching ability of
evolutionary algorithms (EAs) with the predictive ability of surrogate models, are effective to …
evolutionary algorithms (EAs) with the predictive ability of surrogate models, are effective to …
A grouping cooperative differential evolution algorithm for solving partially separable complex optimization problems
Z Chen, J Cao, F Zhao, J Zhang - Cognitive Computation, 2023 - Springer
Differential evolution (DE) is a widely accepted optimization algorithm inspired by the
mechanisms of biological evolution for complex optimization problems. In this paper, we put …
mechanisms of biological evolution for complex optimization problems. In this paper, we put …
Radial basis function-assisted adaptive differential evolution using cooperative dual-phase sampling for high-dimensional expensive optimization problems
To improve the global convergence and optimization efficiency for solving high-dimensional
expensive black-box optimization problems, a radial basis function-assisted adaptive …
expensive black-box optimization problems, a radial basis function-assisted adaptive …
A hierarchical surrogate assisted optimization algorithm using teaching-learning-based optimization and differential evolution for high-dimensional expensive …
Surrogate-assisted evolutionary algorithms (SAEAs) are increasingly used in solving
computationally expensive optimization problems. However, when tackling high …
computationally expensive optimization problems. However, when tackling high …
Surrogate information transfer and fusion in high-dimensional expensive optimization problems
The Kriging surrogate model is less frequently employed in high-dimensional expensive
problems than is the radial basis function (RBF) model. This discrepancy is attributed to the …
problems than is the radial basis function (RBF) model. This discrepancy is attributed to the …