Calibration of liquid crystal ultrafast pulse shaper with common-path spectral interferometry and application to coherent control with a covariance matrix adaptation evolutionary strategy

2008 ◽  
Vol 79 (3) ◽  
pp. 033103 ◽  
Author(s):  
Jesse W. Wilson ◽  
Philip Schlup ◽  
Monte Lunacek ◽  
Darrell Whitley ◽  
Randy A. Bartels
Keyword(s):  
Liquid Crystal ◽  
Covariance Matrix ◽  
Coherent Control ◽  
Evolutionary Strategy ◽  
Pulse Shaper ◽  
Spectral Interferometry ◽  
Common Path ◽  
Covariance Matrix Adaptation ◽  
Ultrafast Pulse

scholarly journals Covariance Matrix Adaptation Evolutionary Strategy for Drift Correction of Electronic Nose Data

2011 ◽  
Author(s):  
S. Di Carlo ◽  
M. Falasconi ◽  
E. Sanchez ◽  
G. Sberveglieri ◽  
A. Scionti ◽  
...  
Keyword(s):  
Covariance Matrix ◽  
Electronic Nose ◽  
Evolutionary Strategy ◽  
Drift Correction ◽  
Covariance Matrix Adaptation

scholarly journals Systematic Comparison of Monte Carlo Annealing and Covariance Matrix Adaptation for the Optimization of ReaxFF Parameters

2018 ◽  
Author(s):  
Ganna Shchygol ◽  
Alexei Yakovlev ◽  
Tomáš Trnka ◽  
Adri C.T. van Duin ◽  
Toon Verstraelen
Keyword(s):  
Monte Carlo ◽  
Force Field ◽  
Covariance Matrix ◽  
General Pattern ◽  
Computational Cost ◽  
Evolutionary Strategy ◽  
Optimal Parameters ◽  
Training Set ◽  
Covariance Matrix Adaptation ◽  
Training Sets

<div>ReaxFF is a computationally efficient force field to simulate complex reactive dynamics in extended molecular models with diverse chemistries, if reliable force-field parameters are available for the chemistry of interest. If not, they must be calibrated by minimizing the error ReaxFF makes on a relevant training set. Because this optimization is far from trivial, many methods, in particular genetic algorithms (GAs), have been developed to search for the global optimum in parameter space. Recently, two alternative parameter calibration techniques were proposed, i.e.\ Monte-Carlo Force Field optimizer (MCFF) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), which have the potential to find good parameters at a relatively low computational cost. In this work, these two methods are tested, as implemented in ADF2018, using three ReaxFF training sets, which have previously been used to benchmark GAs. Even though MCFF and CMA-ES should not be considered as exhaustive global optimizers, they can find parameters that are comparable in quality to those obtained with GAs. We observe that CMA-ES leads to slightly better results and is less sensitive to the initial guess of the parameters. Concrete recipes are provided for obtaining similar results with new training sets.</div><div>Besides optimization recipes, a successful ReaxFF parameterization requires the design of a good training set. At every trial set of parameters, ReaxFF is used to optimize molecular geometries in the training set. When the optimization of some geometries fails easily, it becomes increasingly difficult to find the optimal parameters. We have addressed this issue by fixing several bugs in the ReaxFF forces and by improving the robustness of the geometry optimization. These improvements cannot eliminate all geometry convergence issues and we recommend to avoid very flexible geometries in the training set.</div><div>Both MCFF and CMA-ES are still liable to converge to sub- or near-optimal parameters, which we detected by repeating the calibration with different random seeds. The existence of distinct near-optimal parameter vectors is a general pattern throughout our study and provides opportunities to improve the training set or to detect overfitting artifacts.</div>

Optimization design of unitized panels with stiffeners in different formats using the evolutionary strategy with covariance matrix adaptation

2016 ◽  
Vol 231 (9) ◽  
pp. 1563-1573 ◽  
Author(s):  
Weizhu Yang ◽  
Zhufeng Yue ◽  
Lei Li ◽  
Fan Yang ◽  
Peiyan Wang
Keyword(s):  
Covariance Matrix ◽  
Optimization Design ◽  
Shear Loading ◽  
Evolution Strategy ◽  
Evolutionary Strategy ◽  
Computational Time ◽  
Biaxial Compression ◽  
Loading Conditions ◽  
Design Environment ◽  
Covariance Matrix Adaptation

Curvilinear stiffener concept has been introduced to aircraft panel structures most recently for possible further weight reduction during optimization design. However, due to enlarged design space and high design complexity, more computational time is needed to optimize curvilinearly stiffened panels. Considering the requirement for both lighter structure and less design time, optimization designs of a unitized panel with stiffeners in three different formats, i.e. curvilinear, oblique, and evenly distributed straight, are conducted under various loading conditions to figure out which stiffener format should be selected in a real design environment. Four single loading cases with different compression-shear ratios and a set of multiple load cases are considered. Evolution strategy with covariance matrix adaption is combined with sequential quadratic programming to seek out the optimum structure with minimum mass taking into account buckling and strength constraints. Comparative analysis of the optimization results indicates that evenly distributed straight format is suitable for compression-dominant loading conditions whereas curvilinear format become superior in case that shear loading is considerable or multiple biaxial compression and shear loads are applied. Besides, curvilinear format adds more design flexibilities to the stiffeners, which enables them to play a more important role in the optimized structures. Furthermore, evolution strategy with covariance matrix adaption is found to be more efficient than particle swarm optimization for this optimization problem. This study can provide a useful guidance for future optimization design of aircraft structures.

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