[HTML][HTML] State-of-the-art in aerodynamic shape optimisation methods
SN Skinner, H Zare-Behtash - Applied Soft Computing, 2018 - Elsevier
Aerodynamic optimisation has become an indispensable component for any aerodynamic
design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind …
design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind …
Review of design optimization methods for turbomachinery aerodynamics
Z Li, X Zheng - Progress in Aerospace Sciences, 2017 - Elsevier
In today's competitive environment, new turbomachinery designs need to be not only more
efficient, quieter, and “greener” but also need to be developed at on much shorter time …
efficient, quieter, and “greener” but also need to be developed at on much shorter time …
Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems
Surrogate models have shown to be effective in assisting metaheuristic algorithms for
solving computationally expensive complex optimization problems. The effectiveness of …
solving computationally expensive complex optimization problems. The effectiveness of …
Variable surrogate model-based particle swarm optimization for high-dimensional expensive problems
Many industrial applications require time-consuming and resource-intensive evaluations of
suitable solutions within very limited time frames. Therefore, many surrogate-assisted …
suitable solutions within very limited time frames. Therefore, many surrogate-assisted …
[图书][B] Surrogate-model-based design and optimization
Surrogate-Model-Based Design and Optimization | SpringerLink Skip to main content
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Advertisement SpringerLink Account Menu Find a journal Publish with us Track your research …
Data-driven niching differential evolution with adaptive parameters control for history matching and uncertainty quantification
History matching is a typical inverse problem that adjusts the uncertainty parameters of the
reservoir numerical model with limited dynamic response data. In most situations, various …
reservoir numerical model with limited dynamic response data. In most situations, various …
Multiobjective infill criterion driven Gaussian process-assisted particle swarm optimization of high-dimensional expensive problems
Model management plays an essential role in surrogate-assisted evolutionary optimization
of expensive problems, since the strategy for selecting individuals for fitness evaluation …
of expensive problems, since the strategy for selecting individuals for fitness evaluation …
A two-layer surrogate-assisted particle swarm optimization algorithm
Like most evolutionary algorithms, particle swarm optimization (PSO) usually requires a
large number of fitness evaluations to obtain a sufficiently good solution. This poses an …
large number of fitness evaluations to obtain a sufficiently good solution. This poses an …
Offline data-driven multiobjective optimization: Knowledge transfer between surrogates and generation of final solutions
In offline data-driven optimization, only historical data is available for optimization, making it
impossible to validate the obtained solutions during the optimization. To address these …
impossible to validate the obtained solutions during the optimization. To address these …
An ensemble surrogate-based framework for expensive multiobjective evolutionary optimization
Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling
computationally expensive multiobjective optimization problems (EMOPs), as the surrogate …
computationally expensive multiobjective optimization problems (EMOPs), as the surrogate …