Adaptive Switching of Variable-Fidelity Models in Population-Based Optimization

2015 ◽  
pp. 175-205 ◽  
Author(s):  
Ali Mehmani ◽  
Souma Chowdhury ◽  
Weiyang Tong ◽  
Achille Messac
Keyword(s):  
Population Based ◽  
Variable Fidelity

Fast multi-objective design optimization of microwave and antenna structures using data-driven surrogates and domain segmentation

2019 ◽  
Vol 37 (2) ◽  
pp. 753-788
Author(s):  
Slawomir Koziel ◽  
Adrian Bekasiewicz
Keyword(s):  
Design Optimization ◽  
Design Problem ◽  
Low Cost ◽  
Computational Cost ◽  
Search Space ◽  
Population Based ◽  
Design Framework ◽  
Content Type ◽  
Multi Objective ◽  
Variable Fidelity

Purpose The purpose of this paper is to investigate the strategies and algorithms for expedited design optimization of microwave and antenna structures in multi-objective setup. Design/methodology/approach Formulation of the multi-objective design problem-oriented toward execution of the population-based metaheuristic algorithm within the segmented search space is investigated. Described algorithmic framework exploits variable fidelity modeling, physics- and approximation-based representation of the structure and model correction techniques. The considered approach is suitable for handling various problems pertinent to the design of microwave and antenna structures. Numerical case studies are provided demonstrating the feasibility of the segmentation-based framework for the design of real-world structures in setups with two and three objectives. Findings Formulation of appropriate design problem enables identification of the search space region containing Pareto front, which can be further divided into a set of compartments characterized by small combined volume. Approximation model of each segment can be constructed using a small number of training samples and then optimized, at a negligible computational cost, using population-based metaheuristics. Introduction of segmentation mechanism to multi-objective design framework is important to facilitate low-cost optimization of many-parameter structures represented by numerically expensive computational models. Further reduction of the design cost can be achieved by enforcing equal-volumes of the search space segments. Research limitations/implications The study summarizes recent advances in low-cost multi-objective design of microwave and antenna structures. The investigated techniques exceed capabilities of conventional design approaches involving direct evaluation of physics-based models for determination of trade-offs between the design objectives, particularly in terms of reliability and reduction of the computational cost. Studies on the scalability of segmentation mechanism indicate that computational benefits of the approach decrease with the number of search space segments. Originality/value The proposed design framework proved useful for the rapid multi-objective design of microwave and antenna structures characterized by complex and multi-parameter topologies, which is extremely challenging when using conventional methods driven by population-based metaheuristics algorithms. To the authors knowledge, this is the first work that summarizes segmentation-based approaches to multi-objective optimization of microwave and antenna components.

Variable-Fidelity Optimization With In-Situ Surrogate Model Refinement

2015 ◽  
Author(s):  
Ali Mehmani ◽  
Souma Chowdhury ◽  
Achille Messac
Keyword(s):  
Surrogate Model ◽  
Fitness Function ◽  
Current Model ◽  
Nonlinear Problems ◽  
Pso Algorithm ◽  
Population Based ◽  
Surrogate Models ◽  
Batch Size ◽  
Model Refinement ◽  
Variable Fidelity

Owing to the typical low fidelity of surrogate models, it is often challenging to accomplish reliable optimum solutions in Surrogate-Based Optimization (SBO) for complex nonlinear problems. This paper addresses this challenge by developing a new model-independent approach to refine the surrogate model during optimization, with the objective to maintain a desired level of fidelity and robustness “where” and “when” needed. The proposed approach, called Adaptive Model Refinement (AMR), is designed to work particularly with population-based optimization algorithms. In AMR, reconstruction of the model is performed by sequentially adding a batch of new samples at any given iteration (of SBO) when a refinement metric is met. This metric is formulated by comparing (1) the uncertainty associated with the outputs of the current model, and (2) the distribution of the latest fitness function improvement over the population of candidate designs. Whenever the model-refinement metric is met, the history of the fitness function improvement is used to determine the desired fidelity for the upcoming iterations of SBO. Predictive Estimation of Model Fidelity (an advanced surrogate model error metric) is applied to determine the model uncertainty and the batch size for the samples to be added. The location of the new samples in the input space is determined based on a hypercube enclosing promising candidate designs, and a distance-based criterion that minimizes the correlation between the current sample points and the new points. The powerful mixed-discrete PSO algorithm is used in conjunction with different surrogate models (e.g., Kriging, RBF, SVR) to apply the new AMR method. The performance of the proposed AMR-based SBO is investigated through three different benchmark functions.

Risk of fracture in Crohn's disease: A population-based study

2001 ◽  
Vol 120 (5) ◽  
pp. A628-A628
Author(s):  
E LOFTUSJR ◽  
C CROWSON ◽  
W SANDBORN ◽  
W TREAMINE ◽  
W OFALLON ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Population Based ◽  
Population Based Study ◽  
Risk Of Fracture

1419: Population-Based PSA Velocity

2007 ◽  
Vol 177 (4S) ◽  
pp. 468-468
Author(s):  
David Connolly ◽  
Amanda Black ◽  
Liam J. Murray ◽  
Anna Gavin ◽  
Patrick F. Keane
Keyword(s):  
Population Based ◽  
Psa Velocity

264: Population-Based Study of Renal Cell Carcinoma and Prostate Cancer in the Same Patient

2005 ◽  
Vol 173 (4S) ◽  
pp. 73-73 ◽  
Author(s):  
Daniel A. Barocas ◽  
Farhang Rabbani ◽  
Douglas S. Scherr ◽  
E. Darracott Vaughan
Keyword(s):  
Prostate Cancer ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Population Based ◽  
Population Based Study

535: Population-Based Case-Control Study of PSA and DRE Screening on Prostate Cancer Mortality

2005 ◽  
Vol 173 (4S) ◽  
pp. 146-146
Author(s):  
Eric J. Bergstralh ◽  
Rosebud O. Roberts ◽  
Michael M. Lieber ◽  
Sara A. Farmer ◽  
Jeffrey M. Slezak ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Mortality ◽  
Case Control Study ◽  
Population Based ◽  
Case Control ◽  
Prostate Cancer Mortality ◽  
Control Study
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