scholarly journals Fractional Dynamics of Genetic Algorithms Using Hexagonal Space Tessellation

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
J. A. Tenreiro Machado
Keyword(s):  
Genetic Algorithm ◽  
Distance Measure ◽  
Dimensional Space ◽  
Fitness Function ◽  
Operating Conditions ◽  
Model Parameters ◽  
Fractional Dynamics ◽  
Fourier Domain ◽  
Two Dimensional ◽  
Percolation Phenomena

The paper formulates a genetic algorithm that evolves two types of objects in a plane. The fitness function promotes a relationship between the objects that is optimal when some kind of interface between them occurs. Furthermore, the algorithm adopts an hexagonal tessellation of the two-dimensional space for promoting an efficient method of the neighbour modelling. The genetic algorithm produces special patterns with resemblances to those revealed in percolation phenomena or in the symbiosis found in lichens. Besides the analysis of the spacial layout, a modelling of the time evolution is performed by adopting a distance measure and the modelling in the Fourier domain in the perspective of fractional calculus. The results reveal a consistent, and easy to interpret, set of model parameters for distinct operating conditions.

scholarly journals Automated calibration of a two-dimensional overland flow model by estimating Manning's roughness coefficient using genetic algorithm

2017 ◽  
Vol 20 (2) ◽  
pp. 440-456
Author(s):  
J. Drisya ◽  
D. Sathish Kumar
Keyword(s):  
Genetic Algorithm ◽  
Flow Model ◽  
Overland Flow ◽  
Fitness Function ◽  
Roughness Coefficient ◽  
Model Parameters ◽  
Automatic Data ◽  
Automated Calibration ◽  
Manning’S Roughness Coefficient ◽  
Overland Flow Model

Abstract Calibration is an important phase in the hydrological modelling process. In this study, an automated calibration framework is developed for estimating Manning's roughness coefficient. The calibration process is formulated as an optimization problem and solved using a genetic algorithm (GA). A heuristic search procedure using GA is developed by including runoff simulation process and evaluating the fitness function by comparing the experimental results. The model is calibrated and validated using datasets of Watershed Experimentation System. A loosely coupled architecture is followed with an interface program to enable automatic data transfer between overland flow model and GA. Single objective GA optimization with minimizing percentage bias, root mean square error and maximizing Nash–Sutcliffe efficiency is integrated with the model scheme. Trade-offs are observed between the different objectives and no single set of the parameter is able to optimize all objectives simultaneously. Hence, multi-objective GA using pooled and balanced aggregated function statistic are used along with the model. The results indicate that the solutions on the Pareto-front are equally good with respect to one objective, but may not be suitable regarding other objectives. The present technique can be applied to calibrate the hydrological model parameters.

scholarly journals ESTIMATION OF SEISMIC WAVES PARAMETERS WITH APPLICATION OF A GENETIC ALGORITHM

2018 ◽  
pp. 51-58
Author(s):  
R. R. Sultangaleev ◽  
V. N. Troyan
Keyword(s):  
Genetic Algorithm ◽  
Seismic Waves ◽  
Fitness Function ◽  
Random Number Generator ◽  
Synthetic Data ◽  
Threshold Value ◽  
Velocity Estimation ◽  
Model Parameters ◽  
Basic Step ◽  
The Individual

A Genetic algorithm (GA) is a very important method for the solution of non-linear problems. The basic steps in GA are coding, selection, crossover, mutation and choice. Coding is a way of representing data  in binary notation. The algorithm must determine the fitness of the individual models. This means that  the binary information is decoded into the physical model parameters and the forward problem is solved. The resulting synthetic data is estimated, then compared with the actual observed data using the  specific fitness criteria. The selection of pairs of the individual models for the reproduction is based on  their fitness values. Models with the higher fitness values are more likely to get the selection than models with low  fitness values. A crossover caused the exchange of some information between the paired models thereby  generating new models. The mutation is a random change of binary state. The condition of the procedure of mutation: if a value obtained by a random number generator is less than a certain threshold value, the  mutation procedure is performed. The last basic step in GA is choice. We choose from each pairs a model,  which has the less fitness function. Then we produce the procedures: the crossover, the mutation and the  choice. This procedure is continued until we obtain the optimal model. We have used the GA for the  estimation of the velocity for the gradient layer. The synthetic seismogram was calculated by the finite- difference method. The obtained results showed a high effectiveness of GA for the seismic waves velocity estimation.

scholarly journals Inverse Analysis of the Partitioning Deformation Modulusof High-Arch Dams Based on Quantum Genetic Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yantao Zhu ◽  
Xinqiang Niu ◽  
Jimin Wang ◽  
Chongshi Gu ◽  
Erfeng Zhao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Inverse Analysis ◽  
Fitness Function ◽  
Deformation Modulus ◽  
Operating Conditions ◽  
Hydraulic Structures ◽  
Arch Dams ◽  
Quantum Genetic Algorithm ◽  
Reservoir Basin ◽  
High Arch Dams

The physical and mechanical parameters of hydraulic structures in complicated operating conditions often change over time. Updating these parameters in a timely manner is important to comprehend the operating behaviors and monitor the safety of hydraulic structures. Conventional inverse analysis methods can only generate inversions on the comprehensive deformation modulus of concrete dam structures, which contradict practical conditions. Based on the researches on conventional reversion methods of the deformation modulus of the dam body, foundation, and reservoir basin, the objective fitness function is established in this paper according to engineering-measured data and finite element simulation results. The quantum genetic algorithm has high global search efficiency and population diversity. A mechanical parameter inversion of high-arch dams is built from the intelligent optimization of an established algorithm by applying the quantum genetic algorithm. The proposed algorithm is tested to be feasible and valid for practical engineering projects and therefore shows scientific and practical application values.

Genetic Algorithm Based Optimization Technique for Savonius-Style Wind Turbine

2021 ◽  
Author(s):  
Diplina Paul ◽  
Abhisek Banerjee
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Flow Field ◽  
Wind Turbine ◽  
Fitness Function ◽  
Optimization Technique ◽  
Coefficient Of Performance ◽  
Design Parameters ◽  
Speed Ratio ◽  
Two Dimensional

Abstract In this article, authors have studied genetic algorithm-based optimization technique to optimize rotor profile for elliptic shaped Savonius-style wind turbine with an aim to maximize the coefficient of performance. Genetic algorithm has been used to optimize design variables having distinct values and discontinuous and nondifferentiable objective functions. Optimization procedure using genetic algorithm uses the following steps: initialization, assessment, assortment, crossover and lastly alteration. Once the genetic algorithm is initialized, then the evaluation process trails, where each parametric value is evaluated based on the fitness function stated as objective function. Then the GA operators i.e assortment, cross over and alteration are applied. At the end of GA operation procedure, a new set of values of design parameter is generated. This procedure is endlessly iterated until the convergence criteria is met. Then the optimized and non-optimized profiles are studied using numerical simulation. Initially a two-dimensional numerical model is developed and validated against experimental results. The two-dimensional analysis is conducted using k-ω shear stress transport model. Unsteady Reynold’s Averaged Navier Stoke’s equations have been solved to simulate the flow field of a Savonius-style rotor. This analysis has been executed using finite volume approach in Fluent 17.2 version. Grid independence study is performed to curtail the effect of grid size on the flow field portrayals. The optimization technique implemented on the Savonius-style wind turbine, generated design parameters that were able to yield a coefficient of performance value of 0.398. The coefficient of torque and coefficient of performance values are studied for both optimized and non-optimized profile as a function of tip speed ratio. Numerical simulation predicted a maximum gain of 41% for coefficient of performance at TSR = 1.0 over for optimized profile over the non-optimized profile.

Gearbox condition monitoring using self-organizing feature maps

2004 ◽  
Vol 218 (1) ◽  
pp. 119-129 ◽  
Author(s):  
G Liao ◽  
S Liu ◽  
T Shi ◽  
G Zhang
Keyword(s):  
Condition Monitoring ◽  
Matrix Method ◽  
Early Stage ◽  
Dimensional Space ◽  
Operating Conditions ◽  
Two Dimensional ◽  
Improved Method ◽  
Feature Maps ◽  
Sofm Network ◽  
Self Organizing

This paper proposes a novel technique for the condition monitoring of gearboxes based on a self-organizing feature maps (SOFM) network. In order to visualize the learned SOFM results more clearly, an improved method based on the unified distance matrix (U-matrix) method is presented, in which the overall topological information condensed into the map units is considered so as to project the high-dimensional input vectors into a two-dimensional space and give a better picture of their intrinsic structure than the original U-matrix method. The feature data extracted from industrial gearbox vibration signals measured under different operating conditions are analysed using the proposed technique. The results show that trained with the SOFM network and visualized with the improved method, the feature data are mapped into a two-dimensional space and formed clustering regions, each indicative of a specific gearbox condition. Therefore, the gearbox operating condition with a fatigue crack or broken tooth compared with the normal condition is identified clearly. Furthermore, with the trajectory of the image points for the feature data in two-dimensional space, the variation of gearbox conditions is observed visually, and the development of gearbox early-stage failures is monitored in time.

scholarly journals Parallel Hierarchical Genetic Algorithm for Scattered Data Fitting through B-Splines

2019 ◽  
Vol 9 (11) ◽  
pp. 2336 ◽  
Author(s):  
Jose Edgar Lara-Ramirez ◽  
Carlos Hugo Garcia-Capulin ◽  
Maria de Jesus Estudillo-Ayala ◽  
Juan Gabriel Avina-Cervantes ◽  
Raul Enrique Sanchez-Yanez ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Curve Fitting ◽  
Fitness Function ◽  
Scattered Data ◽  
Model Parameters ◽  
B Spline ◽  
B Splines ◽  
Scattered Data Fitting ◽  
Data Points ◽  
Hierarchical Genetic Algorithm

Curve fitting to unorganized data points is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of scattered and noisy data points, the goal is to construct a curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Although many papers have addressed the problem, this remains very challenging. In this paper we propose to solve the curve fitting problem to noisy scattered data using a parallel hierarchical genetic algorithm and B-splines. We use a novel hierarchical structure to represent both the model structure and the model parameters. The best B-spline model is searched using bi-objective fitness function. As a result, our method determines the number and locations of the knots, and the B-spline coefficients simultaneously and automatically. In addition, to accelerate the estimation of B-spline parameters the algorithm is implemented with two levels of parallelism, taking advantages of the new hardware platforms. Finally, to validate our approach, we fitted curves from scattered noisy points and results were compared through numerical simulations with several methods, which are widely used in fitting tasks. Results show a better performance on the reference methods.

scholarly journals Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Fang Liu ◽  
Jie Ma ◽  
Weixing Su
Keyword(s):  
Genetic Algorithm ◽  
Parameter Identification ◽  
Particle Filter ◽  
Electric Vehicles ◽  
Estimation Method ◽  
Operating Conditions ◽  
Model Parameters ◽  
Improved Genetic Algorithm ◽  
Soc Estimation ◽  
Unscented Particle Filter

In order to solve the problem that the model-based State of Charge (SOC) estimation method is too dependent on the model parameters in the SOC estimation of electric vehicles, an improved genetic algorithm is proposed in this paper. The method has the advantages of being able to quickly determine the search range, reducing the probability of falling into local optimum, and having high recognition accuracy. Then we can realize online dynamic identification of power battery model parameters and improve the accuracy of model parameter identification. In addition, considering the complex application environment and operating conditions of electric vehicles, an SOC estimation method based on improved genetic algorithm and unscented particle filter (improved GA-UPF) is proposed. And we compare the improved GA-UPF algorithm with the least square unscented particle filter (LS-UPF) and improved GA unscented Kalman filter (improved GA-UKF) algorithm. The comparison results show that the improved GA-UPF algorithm proposed in this paper has higher estimation accuracy and better stability. It also reflects the practicability and accuracy of the improved GA parameter identification algorithm proposed in this paper.

scholarly journals Recognition of the Incorrect Values of Measured Parameters at Nuclear Power Plant Operation in Stationary Modes

2018 ◽  
Vol 3 (3) ◽  
pp. 443
Author(s):  
G.V. Chichikin ◽  
S.T. Leskin ◽  
A.Y. Puzakov
Keyword(s):  
Data Structure ◽  
Power Plant ◽  
Nuclear Power ◽  
Input Data ◽  
Dimensional Space ◽  
Operating Conditions ◽  
Primary Circuit ◽  
Two Dimensional ◽  
Power Station ◽  
Power Plant Operation

In this paper, we consider a variant of solving the problem of recognizing incorrect values of the measured parameters on the power unit of a nuclear power station (NPP) using methods of the theory recognition of images. As initial data, the measured parameters of the primary circuit of a VVER NPP in cold and hot operating conditions are used. The analysis of the data structure and localization of the images of the states of the nuclear island is carried out in the two-dimensional space of the principal components. To classify the input data a decision rule is used  . Individual measurements that do not fall into any of the classes are most likely to be attributed to incorrect value. 

scholarly journals Genetic algorithm application for electrodynamic transducer model identification

ScienceRise ◽  
2021 ◽  
pp. 48-57
Author(s):  
Denys Volkov ◽  
Artem Zubkov ◽  
Vitalii Didkovskyi
Keyword(s):  
Genetic Algorithm ◽  
Classical Method ◽  
Fitness Function ◽  
Model Identification ◽  
Added Mass ◽  
Model Parameters ◽  
Identification Scheme ◽  
Different Types ◽  
Scientific Results ◽  
Technological Product

Research object: the adaptation and application of the genetic algorithm for electrodynamic transducer model parameters identification. Investigated problem: to formulate loudspeaker identification task as an optimization problem, adapt it to the genetic algorithm framework and compare obtained results with classical identification method using added mass. Main scientific results: the complete genetic algorithm loudspeaker identification procedure is presented, including: – data acquisition scheme, where the directly measured values for the algorithm application are: voltage at loudspeaker terminals, current through the voice coil and displacement of the moving part – selection of an appropriate set of genes of an individual – derivation of the fitness function for assessing the quality of the identified parameters, which can also be used to identify other types of electroacoustic transducers Also, the advantages of this method in comparison with the classical method of identification using added mass are considered, that are its versatility and ability to quickly configure and adapt for research and experimentation with different loudspeaker models and different types of transducers used in acoustics. Area of practical use of the research results: the proposed genetic loudspeaker model identification scheme can be directly applied on practice to speed up research and development tasks in electroacoustics and other related fields that require frequent experimentation with different types of transducer models. Innovative technological product: genetic algorithm based loudspeaker identification scheme that can be applied to identify various model of electrodynamic transducers. Scope of application of the innovative technological product: electroacoustics, loudspeaker design, audio systems

Correlation averaging of two-dimensional crystals: basic strategy and refinements

1986 ◽  
Vol 44 ◽  
pp. 136-139
Author(s):  
W. Baumeister ◽  
R. Rachel ◽  
R. Guckenberger ◽  
R. Hegerl
Keyword(s):  
Low Dose ◽  
Signal To Noise Ratio ◽  
Real Space ◽  
Fourier Domain ◽  
Two Dimensional ◽  
Signal To Noise ◽  
Unit Cells ◽  
Lateral Displacements ◽  
Established Technique ◽  
Crystalline Array

IntroductionCorrelation averaging (CAV) is meanwhile an established technique in image processing of two-dimensional crystals /1,2/. The basic idea is to detect the real positions of unit cells in a crystalline array by means of correlation functions and to average them by real space superposition of the aligned motifs. The signal-to-noise ratio improves in proportion to the number of motifs included in the average. Unlike filtering in the Fourier domain, CAV corrects for lateral displacements of the unit cells; thus it avoids the loss of resolution entailed by these distortions in the conventional approach. Here we report on some variants of the method, aimed at retrieving a maximum of information from images with very low signal-to-noise ratios (low dose microscopy of unstained or lightly stained specimens) while keeping the procedure economical.

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