A three-level radial basis function method for expensive optimization
This article proposes a three-level radial basis function (TLRBF)-assisted optimization
algorithm for expensive optimization. It consists of three search procedures at each iteration …
algorithm for expensive optimization. It consists of three search procedures at each iteration …
Expected improvement versus predicted value in surrogate-based optimization
Surrogate-based optimization relies on so-called infill criteria (acquisition functions) to
decide which point to evaluate next. When Kriging is used as the surrogate model of choice …
decide which point to evaluate next. When Kriging is used as the surrogate model of choice …
Prediction for manufacturing factors in a steel plate rolling smart factory using data clustering-based machine learning
CY Park, JW Kim, B Kim, J Lee - IEEE Access, 2020 - ieeexplore.ieee.org
A Steel Plate Rolling Mill (SPM) is a milling machine that uses rollers to press hot slab inputs
to produce ferrous or non-ferrous metal plates. To produce high-quality steel plates, it is …
to produce ferrous or non-ferrous metal plates. To produce high-quality steel plates, it is …
On the use of surrogate models in engineering design optimization and exploration: The key issues
Surrogate models are invaluable tools that greatly assist the process of computationally
expensive analyses and optimization. Engineering optimization reaps the benefit from …
expensive analyses and optimization. Engineering optimization reaps the benefit from …
Gaussian process regression ensemble model for network traffic prediction
Network traffic prediction is substantial for network optimization and resource management.
However, designing an efficient predictive model considering different traffic characteristics …
However, designing an efficient predictive model considering different traffic characteristics …
Towards reliable uncertainty quantification via deep ensemble in multi-output regression task
S Yang, K Yee - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
This study aims to comprehensively investigate the deep ensemble approach, an
approximate Bayesian inference, in the multi-output regression task for predicting the …
approximate Bayesian inference, in the multi-output regression task for predicting the …
A sparse multi-fidelity surrogate-based optimization method with computational awareness
CoKriging is a popular surrogate modeling approach to approximate the input–output
relationship using multi-fidelity data from different sources. However, it suffers from the big …
relationship using multi-fidelity data from different sources. However, it suffers from the big …
Surrogate models for discrete optimization problems
M Zaefferer - 2018 - 129.217.131.68
Surrogate models are crucial tools for many real-world optimization problems. An
optimization algorithm can evaluate a data-driven surrogate model instead of an expensive …
optimization algorithm can evaluate a data-driven surrogate model instead of an expensive …
GeoRF: a geospatial random forest
The geospatial domain increasingly relies on data-driven methodologies to extract
actionable insights from the growing volume of available data. Despite the effectiveness of …
actionable insights from the growing volume of available data. Despite the effectiveness of …
Continuous optimization benchmarks by simulation
M Zaefferer, F Rehbach - Parallel Problem Solving from Nature–PPSN XVI …, 2020 - Springer
Benchmark experiments are required to test, compare, tune, and understand optimization
algorithms. Ideally, benchmark problems closely reflect real-world problem behavior. Yet …
algorithms. Ideally, benchmark problems closely reflect real-world problem behavior. Yet …