Predicting effective control parameters for differential evolution using cluster analysis of objective function features

A methodology is introduced which uses three simple objectivefunction features to predict effective control parameters for differential evolution. This is achieved using cluster analysis techniques to classify objectivefunctions using these features. Information on prior performance of variouscontrol parameters for each classification is then used to determine which control parameters to use in future optimisations. Our approach is compared tostate–of–the–art adaptive and non–adaptive techniques. Two accepted benchmark suites are used to compare performance and in all cases we show thatthe improvement resulting from our approach is statistically significant. Themajority of the computational effort of this methodology is performed off–line, however even when taking into account the additional on–line cost ourapproach outperforms other adaptive techniques. We also study the key tuning parameters of our methodology, such as number of clusters, which furthersupport the finding that the simple features selected are predictors of effectivecontrol parameters. The findings presented in this paper are significant becausethey show that simple to calculate features of objective functions can help toselect control parameters for optimisation algorithms. This can have an immediate positive impact the application of these optimisation algorithms on realworld problems where it is often difficult to select effective control parameters

Numerical validation of a population balance model describing cement paste rheology

Rheology control is essential during the period in which cement and concrete pastes are encountered in the fresh state, due to the fact that it directly affects workability, initial placement and the structural performance of the hardened material. Optimizations of clinker formulations and reductions in cement-to-water ratios induced by economic and environmental considerations have a significant effect in rheology, which invokes the need for mechanistic models capable of describing the effect of multiple relevant phenomena on the observed paste flow. In this work, the population balance framework was implemented to develop a model able to relate the transient microstructural evolution of cement pastes under typical experimental conditions with its macroscopic rheological responses. Numerical details and performance are assessed and discussed. It was found that the model is capable of reproducing experimentally observed flow curves by using measured cluster size distribution information. It is also able to predict the complex rheological characteristics typically found in cement pastes. Furthermore, a spatially resolved scheme was proposed to investigate the nature of flow inside a parallel-plates rheometer geometry with the objective of assessing the ability of the model of qualitatively predicting experimentally observed behavior and to gain insight into the effect of possible secondary flows

Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data

The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Is One Hyperparameter Optimizer Enough?

Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics. To address this gap in the literature, this paper applied a range of hyperparameter optimizers (grid search, random search, differential evolution, and Bayesian optimization) to defect prediction problem. Surprisingly, no hyperparameter optimizer was observed to be `best' and, for one of the two evaluation measures studied here (F-measure), hyperparameter optimization, in 50\% cases, was no better than using default configurations. We conclude that hyperparameter optimization is more nuanced than previously believed. While such optimization can certainly lead to large improvements in the performance of classifiers used in software analytics, it remains to be seen which specific optimizers should be applied to a new dataset.Comment: 7 pages, 2 columns, accepted for SWAN1

Training Echo State Networks with Regularization through Dimensionality Reduction

In this paper we introduce a new framework to train an Echo State Network to predict real valued time-series. The method consists in projecting the output of the internal layer of the network on a space with lower dimensionality, before training the output layer to learn the target task. Notably, we enforce a regularization constraint that leads to better generalization capabilities. We evaluate the performances of our approach on several benchmark tests, using different techniques to train the readout of the network, achieving superior predictive performance when using the proposed framework. Finally, we provide an insight on the effectiveness of the implemented mechanics through a visualization of the trajectory in the phase space and relying on the methodologies of nonlinear time-series analysis. By applying our method on well known chaotic systems, we provide evidence that the lower dimensional embedding retains the dynamical properties of the underlying system better than the full-dimensional internal states of the network