Surrogate-assisted evolutionary algorithm with model and infill criterion auto-configuration
Surrogate-assisted evolutionary algorithms (SAEAs) have proven to be effective in solving
computationally expensive optimization problems (EOPs). However, the performance of …
computationally expensive optimization problems (EOPs). However, the performance of …
Dynamic niching particle swarm optimization with an external archive-guided mechanism for multimodal multi-objective optimization
Y Sun, Y Chang, S Yang, F Wang - Information Sciences, 2024 - Elsevier
Multimodal multi-objective optimization problems (MMOPs) contain multiple equivalent
Pareto optimal sets (PSs) corresponding to the same Pareto front (PF). However …
Pareto optimal sets (PSs) corresponding to the same Pareto front (PF). However …
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 …
A kriging-assisted double population differential evolution for mixed-integer expensive constrained optimization problems with mixed constraints
Many surrogate-assisted evolutionary algorithms (SAEA) with outstanding performance
have been developed to handle Expensive Constrained Optimization Problems (ECOPs) …
have been developed to handle Expensive Constrained Optimization Problems (ECOPs) …
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 …
A global-local hybrid strategy with adaptive space reduction search method for structural health monitoring
G Zhang, C Wan, S Xue, L Xie - Applied mathematical modelling, 2023 - Elsevier
The difficulty in computational convergence poses challenges of application for traditional
heuristic optimization algorithms to solve the optimization-based structural identification …
heuristic optimization algorithms to solve the optimization-based structural identification …
Multi-surrogate-assisted stochastic fractal search based on scale-free network for high-dimensional expensive optimization
Surrogate-assisted meta-heuristic algorithms (SAMAs) have been increasingly popular in
recent years for solving challenging optimization problems. However, the majority of recent …
recent years for solving challenging optimization problems. However, the majority of recent …
A dynamic aggregation strategy enhanced efficient global optimization algorithm for solving high-dimensional turbomachinery design problems
Q Wang, Z Guo, Y Chen, G Ma, L Song… - Engineering …, 2024 - Taylor & Francis
To address challenges effectively in turbomachinery design optimization involving high-
dimensional (d≥ 30) expensive black-box problems, a dedicated Efficient Global …
dimensional (d≥ 30) expensive black-box problems, a dedicated Efficient Global …
Emulation-based adaptive differential evolution: fast and auto-tunable approach for moderately expensive optimization problems
K Nishihara, M Nakata - Complex & Intelligent Systems, 2024 - Springer
In the field of expensive optimization, numerous papers have proposed surrogate-assisted
evolutionary algorithms (SAEAs) for a few thousand or even hundreds of function …
evolutionary algorithms (SAEAs) for a few thousand or even hundreds of function …
Dual-state-driven evolutionary optimization for expensive optimization problems with continuous and categorical variables
The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient
approaches for addressing expensive continuous or combinatorial optimization problems …
approaches for addressing expensive continuous or combinatorial optimization problems …