Journal Article10.1080/0305215X.2013.786063
A multi-objective variable-fidelity optimization method for genetic algorithms
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TL;DR: In this article, a variable-fidelity optimization (VFO) scheme for multi-objective genetic algorithms is presented, which uses a low and high fidelity version of the objective function with a Kriging scaling model to interpolate between them.
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Abstract: A novel variable-fidelity optimization (VFO) scheme is presented for multi-objective genetic algorithms. The technique uses a low- and high-fidelity version of the objective function with a Kriging scaling model to interpolate between them. The Kriging model is constructed online through a fixed updating schedule. Results for three standard genetic algorithm test cases and a two-objective stiffened panel optimization problem are presented. For the stiffened panel problem, statistical analysis of four performance metrics are used to compare the Pareto fronts between the VFO method, full high-fidelity optimizer runs, and Pareto fronts developed by enumeration. The fixed updating approach is shown to reduce the number of high-fidelity calls significantly while approximating the Pareto front in an efficient manner.
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Citations
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms
Tinkle Chugh,Karthik Sindhya,Jussi Hakanen,Kaisa Miettinen +3 more
- 01 May 2019
TL;DR: A survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems and identifies and discusses some promising elements and major issues among algorithms in the Literature related to using an approximation and numerical settings used.
A robust optimization approach based on multi-fidelity metamodel
TL;DR: A MF metamodel assisted robust optimization approach is developed, in which the interpolation uncertainty of the MF metAModel and design variable uncertainty are quantified and taken into consideration and can obtain a solution that is both optimal and within the feasible region even with perturbation of the uncertain variables.
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Two infill criteria driven surrogate-assisted multi-objective evolutionary algorithms for computationally expensive problems with medium dimensions
TL;DR: A surrogate-assisted dominance-based multi- objective evolutionary algorithm to solve multi-objective computationally expensive problems with medium dimensions that significantly outperforms some state-of-the-art evolutionary algorithms on most problems.
49
Improving surrogate-assisted variable fidelity multi-objective optimization using a clustering algorithm
Yan Liu,Matthew Collette +1 more
- 01 Nov 2014
TL;DR: In this article, the authors extended variable fidelity optimization framework to include multiple surrogates and used a k-means clustering algorithm to partition model data into local surrogate models to solve the large sample size surrogate-modeling problem.
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Surrogate-Model-Based Design and Optimization
Ping Jiang,Qi Zhou,Xinyu Shao +2 more
- 01 Jan 2020
TL;DR: Since most engineering design problems involve time-consuming simulations and analysis, surrogate models are often used for fast calculations, sensitivity analysis, exploring the design space and supporting optimal design.
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Kalyanmoy Deb,Deb Kalyanmoy +1 more
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TL;DR: This paper presents a meta-modelling framework for estimating Output from Computer Experiments-Predicting Output from Training Data and Criteria Based Designs for computer Experiments.
Design and analysis of computer experiments
Sonja Kuhnt,David M. Steinberg +1 more
TL;DR: The included papers present an interesting mixture of recent developments in the field as they cover fundamental research on the design of experiments, models and analysis methods as well as more applied research connected to real-life applications.
Metamodels for Computer-Based Engineering Design: Survey and Recommendations
TL;DR: This paper surveys their existing application in engineering design, and addresses the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes, along with recommendations for the appropriate use of statistical approximation techniques in given situations.