scholarly journals Using a Genetic Algorithm to Optimize Configurations in a Data-Driven Application

2020 ◽  
pp. 137-152
Author(s):  
Urjoshi Sinha ◽  
Mikaela Cashman ◽  
Myra B. Cohen
Keyword(s):  
Genetic Algorithm ◽  
Data Driven

scholarly journals Data-Driven Tuning of PID Controlled Piezoelectric Ultrasonic Motor

Actuators ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 148
Author(s):  
Sarah Makarem ◽  
Bülent Delibas ◽  
Burhanettin Koc
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Good Alternative ◽  
Data Driven ◽  
Grid Search ◽  
Single Source ◽  
Dual Frequency ◽  
Dual Source ◽  
Stage System ◽  
Piezoelectric Ultrasonic Motor

Ultrasonic motors employ resonance to amplify the vibrations of piezoelectric actuator, offering precise positioning and relatively long travel distances and making them ideal for robotic, optical, metrology and medical applications. As operating in resonance and force transfer through friction lead to nonlinear characteristics like creep and hysteresis, it is difficult to apply model-based control, so data-driven control offers a good alternative. Data-driven techniques are used here for iterative feedback tuning of a proportional integral derivative (PID) controller parameters and comparing between different motor driving techniques, single source and dual source dual frequency (DSDF). The controller and stage system used are both produced by the company Physik Instrumente GmbH, where a PID controller is tuned with the help of four search methods: grid search, Luus–Jaakola method, genetic algorithm, and a new hybrid method developed that combines elements of grid search and Luus–Jaakola method. The latter method was found to be quick to converge and produced consistent result, similar to the Luus–Jaakola method. Genetic Algorithm was much slower and produced sub optimal results. The grid search has also proven the DSDF driving method to be robust, less parameter dependent, and produces far less integral position error than the single source driving method.

scholarly journals A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm

2020 ◽  
Author(s):  
Jinjun Tang ◽  
Yifan Yang ◽  
Wei Hao ◽  
Fang Liu ◽  
Yinhai Wang
Keyword(s):  
Genetic Algorithm ◽  
Data Driven ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Urban Bus ◽  
Timetable Optimization

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Data-Driven Bi-Objective Genetic Algorithms EvoNN and BioGP and Their Applications in Metallurgical and Materials Domain

2016 ◽  
pp. 346-368 ◽  
Author(s):  
Nirupam Chakraborti
Keyword(s):  
Genetic Algorithm ◽  
Noisy Data ◽  
Prey Type ◽  
Data Driven ◽  
Neural Net ◽  
Predator Prey ◽  
Modeling And Optimization ◽  
Materials Research ◽  
Computational Materials ◽  
Data Driven Modeling

Data-driven modeling and optimization are now of utmost importance in computational materials research. This chapter presents the operational details of two recent algorithms EvoNN (Evolutionary Neural net) and BioGP (Bi-objective Genetic Programming) which are particularly suitable for modeling and optimization tasks pertinent to noisy data. In both the approaches a tradeoff between the accuracy and complexity of the candidate models are sought, ultimately leading to some optimum tradeoffs. These novel strategies are tailor-made for constructing models of right complexity, excluding the non-essential inputs. They are constructed to implement the notion of Pareto-optimality using a predator-prey type genetic algorithm, providing the user with a set of optimum models, out of which an appropriate one can be easily picked up by applying some external criteria, if necessary. Several materials related problems have been solved using these algorithms in recent times and a couple of typical examples are briefly presented in this chapter.

Dynamic Data-Driven Genetic Algorithm for forest fire spread prediction

2012 ◽  
Vol 3 (5) ◽  
pp. 398-404 ◽  
Author(s):  
Mónica Denham ◽  
Kerstin Wendt ◽  
Germán Bianchini ◽  
Ana Cortés ◽  
Tomàs Margalef
Keyword(s):  
Genetic Algorithm ◽  
Forest Fire ◽  
Fire Spread ◽  
Data Driven ◽  
Dynamic Data

Data-driven Prestack Waveform Inversion Using Genetic Algorithm- Methodology and Examples

2021 ◽  
pp. 1-97
Author(s):  
Lingxiao Jia ◽  
Subhashis Mallick ◽  
Cheng Wang
Keyword(s):  
Genetic Algorithm ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Seismic Inversion ◽  
P Wave ◽  
Data Driven ◽  
Initial Model ◽  
Reservoir Properties ◽  
Well Control ◽  
Seismic Waveform

The choice of an initial model for seismic waveform inversion is important. In matured exploration areas with adequate well control, we can generate a suitable initial model using well information. However, in new areas where well control is sparse or unavailable, such an initial model is compromised and/or biased by the regions with more well controls. Even in matured exploration areas, if we use time-lapse seismic data to predict dynamic reservoir properties, an initial model, that we obtain from the existing preproduction wells could be incorrect. In this work, we outline a new methodology and workflow for a nonlinear prestack isotropic elastic waveform inversion. We call this method a data driven inversion, meaning that we derive the initial model entirely from the seismic data without using any well information. By assuming a locally horizonal stratification for every common midpoint and starting from the interval P-wave velocity, estimated entirely from seismic data, our method generates pseudo wells by running a two-pass one-dimensional isotropic elastic prestack waveform inversion that uses the reflectivity method for forward modeling and genetic algorithm for optimization. We then use the estimated pseudo wells to build the initial model for seismic inversion. By applying this methodology to real seismic data from two different geological settings, we demonstrate the usefulness of our method. We believe that our new method is potentially applicable for subsurface characterization in areas where well information is sparse or unavailable. Additional research is however necessary to improve the compute-efficiency of the methodology.

Application of Machine Learning Techniques for Real Time Rate of Penetration Optimization

2021 ◽  
Author(s):  
Asad Mustafa Elmgerbi ◽  
Clemens Peter Ettinger ◽  
Peter Mbah Tekum ◽  
Gerhard Thonhauser ◽  
Andreas Nascimento
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Real Time ◽  
Predictive Models ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Time Rate ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Learning Techniques

Abstract Over the past decade, several models have been generated to predict Rate of Penetration (ROP) in real-time. In general, these models can be classified into two categories, model-driven (analytical models) and data-driven models (based on machine learning techniques), which is considered as cutting-edge technology in terms of predictive accuracy and minimal human interfering. Nevertheless, most existing machine learning models are mainly used for prediction, not optimization. The ROP ahead of the bit for a certain formation layer can be predicted with such methods, but the limitation of the applications of these techniques is to find an optimum set of operating parameters for the optimization of ROP. In this regard, two data-driven models for ROP prediction have been developed and thereafter have been merged into an optimizer model. The purpose of the optimization process is to seek the ideal combinations of drilling parameters that would lead to an improvement in the ROP in real-time for a given formation. This paper is mainly focused on describing the process of development to create smart data-driven models (built on MATLAB software environment) for real-time rate of penetration prediction and optimization within a sufficient time span and without disturbing the drilling process, as it is typically required by a drill-off test. The used models here can be classified into two groups: two predictive models, Artificial Neural Network (ANN) and Random Forest (RF), in addition to one optimizer, namely genetic algorithm. The process started by developing, optimizing, and validation of the predictive models, which subsequently were linked to the genetic algorithm (GA) for real-time optimization. Automated optimization algorithms were integrated into the process of developing the productive models to improve the model efficiency and to reduce the errors. In order to validate the functionalities of the developed ROP optimization model, two different cases were studied. For the first case, historical drilling data from different wells were used, and the results confirmed that for the three known controllable surface drilling parameters, weight on bit (WOB) has the highest impact on ROP, followed by flow rate (FR) and finally rotation per minute (RPM), which has the least impact. In the second case, a laboratory scaled drilling rig "CDC miniRig" was utilized to validate the developed model, during the validation only the previous named parameters were used. Several meters were drilled through sandstone cubes at different weights on bit, rotations per minute, and flow rates to develop the productive models; then the optimizer was activated to propose the optimal set of the used parameters, which likely maximize the ROP. The proposed parameters were implemented, and the results showed that ROP improved as expected.

scholarly journals A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm

2020 ◽  
Author(s):  
Jinjun Tang ◽  
Yang Yifan ◽  
Wei Hao ◽  
Fang Liu ◽  
Yinhai Wang
Keyword(s):  
Genetic Algorithm ◽  
Data Driven ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Urban Bus ◽  
Timetable Optimization

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