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Wyszukujesz frazę "Genetic algorithm" wg kryterium: Temat


Wyświetlanie 1-12 z 12
Tytuł:
Multi-variable optimization of an ytterbium-doped fiber laser using genetic algorithm
Autorzy:
Hashemi, S. S.
Ghavami, S. S.
Khorsandi, A
Powiązania:
https://bibliotekanauki.pl/articles/175086.pdf
Data publikacji:
2015
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
fiber laser
optimization
genetic algorithm
Opis:
We introduce the genetic algorithm for the optimization of an Yb3+-doped double-clad fiber laser based on a multi-variable scheme. The output characteristic of the laser is numerically simulated using real practical values. This is performed through solving the associated steady-state rate equation and investigating the effects of input variables such as pump and signal wavelengths and length of the fiber on the laser output. It is found that pumping of the medium around 975 nm is conducted to attain the maximum output power of ~34.8 W, while the stability of the outcoupled power is significantly improved when pumping at 920 nm, confirming good agreement with the reported experimental results. We have also found that by using genetic algorithm base multi-variable optimization, the output power can be significantly increased by about three orders of magnitude and reaches to ~28.5 W with optimum and shorter fiber length of ~57.5 m. Obtained results show that based on the genetic algorithm multi-variable discipline, fiber characteristics can be optimized according to the gaining of maximum output power.
Źródło:
Optica Applicata; 2015, 45, 3; 355-367
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Improving estimation accuracy of metallurgical performance of industrial flotation process by using hybrid genetic algorithm – artificial neural network (GA-ANN)
Autorzy:
Allahkarami, E.
Salmani Nuri, O.
Abdollahzadeh, A.
Rezai, B.
Maghsoudi, B.
Powiązania:
https://bibliotekanauki.pl/articles/109424.pdf
Data publikacji:
2017
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
artificial neural network
genetic algorithm
prediction
copper flotation
Opis:
In this study, a back propagation feed forward neural network, with two hidden layers (10:10:10:4), was applied to predict Cu grade and recovery in industrial flotation plant based on pH, chemical reagents dosage, size percentage of feed passing 75 μm, moisture content in feed, solid ratio, and grade of copper, molybdenum, and iron in feed. Modeling is performed basing on 92 data sets under different operating conditions. A back propagation training was carried out with initial weights randomly mode that may lead to trapping artificial neural network (ANN) into the local minima and converging slowly. So, the genetic algorithm (GA) is combined with ANN for improving the performance of the ANN by optimizing the initial weights of ANN. The results reveal that the GA-ANN model outperforms ANN model for predicting of the metallurgical performance. The hybrid GA-ANN based prediction method, as used in this paper, can be further employed as a reliable and accurate method, in the metallurgical performance prediction.
Źródło:
Physicochemical Problems of Mineral Processing; 2017, 53, 1; 366-378
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Simultaneous optimization of flotation column performance using genetic evolutionary algorithm
Autorzy:
Nakhaei, F.
Irannajad, M.
Yousefikhoshbakht, M.
Powiązania:
https://bibliotekanauki.pl/articles/110806.pdf
Data publikacji:
2016
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
flotation column
optimization
genetic algorithm
non-linear regression
upgrading curve
Opis:
Column flotation is a multivariable process. Its optimization guarantees the metallurgical yield of the process, expressed by the grade and recovery of the concentrate. The present work aimed at applying genetic algorithms (GAs) to optimize a pilot column flotation process which is characterized by being difficult to be optimized via conventional methods. A non-linear mathematical model was used to describe the dynamic behavior of the multivariable process. The solution of the optimization problem using conventional algorithms does not always lead to convergence because of the high dimensionality and non-linearity of the model. In order to deal with this process, the use of a genetic evolutionary algorithm is justified. In this way, GA was coupled with the multivariate non-linear regression (MNLR) of the column flotation metallurgical performance as a fitting function in order to optimize the column flotation process. Then, this kind of intelligent approach was verified by using mineral processing approaches such as Halbich’s upgrading curve. The aim of the optimization through GAs was searching for the process inputs that maximize the productivity of copper in the Sarcheshmeh pilot plant. In this case, the simulation optimization problem was defined as finding the best values for the froth height, chemical reagent dosage, wash water, air flow rate, air holdup, and Cu grade in rougher and column feed streams. The results indicated that GA was a robust and powerful search method to find the best values of the flotation column model parameters that lead to more reliable simulation predictions at a reasonable time. Based on the grade–recovery Halbich upgrading curve, the MNLR model coupled with GA can be used for determination of the flotation optimum conditions.
Źródło:
Physicochemical Problems of Mineral Processing; 2016, 52, 2; 874-893
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Numerical analysis of tailing dam with calibration based on genetic algorithm and geotechnical monitoring data
Autorzy:
Grosel, Szczepan
Powiązania:
https://bibliotekanauki.pl/articles/1845160.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
soil parameters
optimization
slope stability
genetic algorithm
observational method
monitoring
Opis:
The article presents a method of calibration of material parameters of a numerical model based on a genetic algorithm, which allows to match the calculation results with measurements from the geotechnical monitoring network. This method can be used for the maintenance of objects managed by the observation method, which requires continuous monitoring and design alterations. The correctness of the calibration method has been verified on the basis of artificially generated data in order to eliminate inaccuracies related to approximations resulting from the numerical model generation. Using the example of the tailing dam model the quality of prediction of the selected measurement points was verified. Moreover, changes of factor of safety values, which is an important indicator for designing this type of construction, were analyzed. It was decided to exploit the case of dam of reservoir, which is under continuous construction, that is dam height is increasing constantly, because in this situation the use of the observation method is relevant.
Źródło:
Studia Geotechnica et Mechanica; 2021, 43, 1; 34-47
0137-6365
2083-831X
Pojawia się w:
Studia Geotechnica et Mechanica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Construction of Variable Strength Covering Array for Combinatorial Testing Using a Greedy Approach to Genetic Algorithm
Autorzy:
Bansal, P
Sabharwal, S
Mittal, N
Arora, S
Powiązania:
https://bibliotekanauki.pl/articles/384137.pdf
Data publikacji:
2015
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
combinatorial testing
variable strength covering array
genetic algorithm
greedy approach
Opis:
The limitation of time and budget usually prohibits exhaustive testing of interactions between components in a component based software system. Combinatorial testing is a software testing technique that can be used to detect faults in a component based software system caused by the interactions of components in an effective and efficient way. Most of the research in the field of combinatorial testing till now has focused on the construction of optimal covering array (CA) of fixed strength t which covers all t-way interactions among components. The size of CA increases with the increase in strength of testing t, which further increases the cost of testing. However, not all components require higher strength interaction testing. Hence, in a system with k components a technique is required to construct CA of fixed strength t which covers all t-way interactions among k components and all ti-way (where ti > t) interactions between a subset of k components. This is achieved using the variable strength covering array (VSCA). In this paper we propose a greedy based genetic algorithm (GA) to generate optimal VSCA. Experiments are conducted on several benchmark configurations to evaluate the effectiveness of the proposed approach.
Źródło:
e-Informatica Software Engineering Journal; 2015, 9, 1; 87-105
1897-7979
Pojawia się w:
e-Informatica Software Engineering Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Locating critical circular and unconstrained failure surface in slope stability analysis with tailored genetic algorithm
Autorzy:
Pasik, T.
van der Meij, R.
Powiązania:
https://bibliotekanauki.pl/articles/178579.pdf
Data publikacji:
2017
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
genetic algorithm
Spencers method
slope stability
critical failure surface
uplift phenomenon
Opis:
This article presents an efficient search method for representative circular and unconstrained slip surfaces with the use of the tailored genetic algorithm. Searches for unconstrained slip planes with rigid equilibrium methods are yet uncommon in engineering practice, and little publications regarding truly free slip planes exist. The proposed method presents an effective procedure being the result of the right combination of initial population type, selection, crossover and mutation method. The procedure needs little computational effort to find the optimum, unconstrained slip plane. The methodology described in this paper is implemented using Mathematica. The implementation, along with further explanations, is fully presented so the results can be reproduced. Sample slope stability calculations are performed for four cases, along with a detailed result interpretation. Two cases are compared with analyses described in earlier publications. The remaining two are practical cases of slope stability analyses of dikes in Netherlands. These four cases show the benefits of analyzing slope stability with a rigid equilibrium method combined with a genetic algorithm. The paper concludes by describing possibilities and limitations of using the genetic algorithm in the context of the slope stability problem.
Źródło:
Studia Geotechnica et Mechanica; 2017, 39, 4; 87-98
0137-6365
2083-831X
Pojawia się w:
Studia Geotechnica et Mechanica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction and optimization of tower mill grinding power consumption based on GA-BP neural network
Autorzy:
Wang, Ziyang
Hou, Ying
Sobhy, Ahmed
Powiązania:
https://bibliotekanauki.pl/articles/27323660.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
tower mill
grinding power consumption
energy saving
genetic algorithm
BP neural network
Opis:
Grinding is commonly responsible for the liberation of valuable minerals from host rocks but can entail high costs in terms of energy and medium consumption, but a tower mill is a unique power-saving grinding machine over traditional mills. In a tower mill, many operating parameters affect the grinding performance, such as the amount of slurry with a known solid concentration, screw mixer speed, medium filling rate, material-ball ratio, and medium properties. Thus, 25 groups of grinding tests were conducted to establish the relationship between the grinding power consumption and operating parameters. The prediction model was established based on the backpropagation “BP” neural network, further optimized by the genetic algorithm GA to ensure the accuracy of the model, and verified. The test results show that the relative error of the predicted and actual values of the backpropagation “BP” neural network prediction model within 3% was reduced to within 2% by conducting the generic algorithm backpropagation “GA-BP” neural network. The optimum grinding power consumption of 41.069 kWh/t was obtained at the predicted operating parameters of 66.49% grinding concentration, 301.86 r/min screw speed, 20.47% medium filling rate, 96.61% medium ratio, and 0.1394 material-ball ratio. The verifying laboratory test at the optimum conditions, produced a grinding power consumption of 41.85 kWh/t with a relative error of 1.87%, showing the feasibility of using the genetic algorithm and BP neural network to optimize the grinding power consumption of the tower mill.
Źródło:
Physicochemical Problems of Mineral Processing; 2023, 59, 6; art. no. 172096
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An exoskeleton arm optimal configuration determination using inverse kinematics and genetic algorithm
Autorzy:
Głowiński, Sebastian
Błażejewski, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/306968.pdf
Data publikacji:
2019
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
algorytm genetyczny
kinematyka odwrotna
egzoszkielet
ramię
arm exoskeleton
genetic algorithm
inverse kinematics
Opis:
This paper deals with the kinematic modelling of an arm exoskeleton used for human rehabilitation. The biomechanics of the arm was studied and the 9 Degrees of Freedom model was obtained. The particular (optimal) exoskeleton arm configuration is needed, depending on patient abilities and possibility or other users activity. Methods: The model of upper arm was obtained by using Denavit–Hartenberg notation. The exoskeleton human arm was modelled in MathWorks package. The multicriteria optimization procedure was formulated to plan the motion of trajectory. In order to find the problem solution, an artificial intelligence method was used. Results: The optimal solutions were found applying a genetic algorithm. Two variants of motion with and the visualization of the change of joints angles were shown. By the use of genetic algorithms, movement trajectory with the Pareto-optimum solutions has been presented as well. Creating a utopia point, it was possible to select only one solution from Pareto-optimum results. Conclusions: The obtained results demonstrate the efficiency of the proposed approach that can be utilized to analyse the kinematics and dynamics of exoskeletons using the dedicated design process. Genetic algorithm solution could be implemented to command actuators, especially in the case of multi-criteria problems. Moreover, the effectiveness of this method should be evaluated in the future by real experiments.
Źródło:
Acta of Bioengineering and Biomechanics; 2019, 21, 1; 45-53
1509-409X
2450-6303
Pojawia się w:
Acta of Bioengineering and Biomechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Selected Nature-Inspired Algorithms in Function of PM Synchronous Motor Cogging Torque Minimisation
Autorzy:
Cvetkovski, Goga Vladimir
Petkovska, Lidija
Powiązania:
https://bibliotekanauki.pl/articles/1955984.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
permanent magnet synchronous motor
optimisation method
cogging torque
genetic algorithm
finite element method
Opis:
Both permanent magnet brushless DC motors and permanent magnet synchronous motors have attracted wide attention and are increasingly used in industrial high-performance applications in recent years. Those motors are known for their good electrical, magnetic and performance characteristics, but there is one parameter known as cogging torque that has a negative influence on the performance characteristics of the motor. This pulsating torque is generated as a result of the interaction between the stator teeth and the permanent magnets. The minimisation of the ripple of this torque in those permanent magnet motors is of great importance and is generally achieved by a special motor design which in the design process involves a variety of many geometrical motor parameters. In this research work, a novel approach will be introduced where two different nature-inspired algorithms, such as genetic algorithm (GA) and cuckoo search (CS) algorithm are used as an optimisation tool, in which the defined equation for the maximum value of the cogging torque is applied as an objective function. Therefore, a proper mathematical presentation of the maximum value of the cogging torque for the analysed synchronous motor is developed and implemented in the research work. For a detailed analysis of the three different motor models, the initial motor and the two optimised motor models are modelled and analysed using a finite element method approach. The cogging torque is analytically and numerically calculated and the results for all the models are presented.
Źródło:
Power Electronics and Drives; 2021, 6, 41; 204-217
2451-0262
2543-4292
Pojawia się w:
Power Electronics and Drives
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The kernel and shell structure as a tool for improving the graph of transportation connections
Autorzy:
Mażbic-Kulma, B.
Owsiński, J. W.
Sęp, K.
Stańczak, J.
Powiązania:
https://bibliotekanauki.pl/articles/406561.pdf
Data publikacji:
2013
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
transport
connections graph
hub and spoke
kernel and shell
α-clique
genetic algorithm
Opis:
A model of a transportation system is expected to be useful in simulations of a real system to solve given transportation tasks. A connection graph is routinely used to describe a transportation system. Vertices can be train stations, bus stops, airports etc. The edges show direct connections between vertices. A direct approach can be difficult and computational problems can arise in attempts to organize or optimize such a transportation system. Therefore, a method for aggregating such graphs was introduced, using a general kernel and shell structure and its particular instances: α-clique structured graphs of connections and a hub and spoke transformation of the source graph. These structures enable the concentration and ordering of transport between vertices and reduction of the analyzed graph. To obtain the desired structures, several versions of a specialized evolutionary algorithm were developed and applied.
Źródło:
Operations Research and Decisions; 2013, 23, 2; 91-105
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Numerical verification of two-component dental implant in the context of fatigue life for various load cases
Autorzy:
Szajek, K.
Wierszycki, M.
Powiązania:
https://bibliotekanauki.pl/articles/307214.pdf
Data publikacji:
2016
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
implant stomatologiczny
optymalizacja
trwałość zmęczeniowa
algorytm genetyczny
dental implant
optimization
fatigue life
genetic algorithm
Opis:
Purpose: Dental implant designing is a complex process which considers many limitations both biological and mechanical in nature. In earlier studies, a complete procedure for improvement of two-component dental implant was proposed. However, the optimization tasks carried out required assumption on representative load case, which raised doubts on optimality for the other load cases. This paper deals with verification of the optimal design in context of fatigue life and its main goal is to answer the question if the assumed load scenario (solely horizontal occlusal load) leads to the design which is also “safe” for oblique occlussal loads regardless the angle from an implant axis. Methods: The verification is carried out with series of finite element analyses for wide spectrum of physiologically justified loads. The design of experiment methodology with full factorial technique is utilized. All computations are done in Abaqus suite. Results: The maximal Mises stress and normalized effective stress amplitude for various load cases are discussed and compared with the assumed “safe” limit (equivalent of fatigue life for 5e6 cycles). Conclusions: The obtained results proof that coronial-appical load component should be taken into consideration in the two component dental implant when fatigue life is optimized. However, its influence in the analyzed case is small and does not change the fact that the fatigue life improvement is observed for all components within whole range of analyzed loads.
Źródło:
Acta of Bioengineering and Biomechanics; 2016, 18, 1; 103-113
1509-409X
2450-6303
Pojawia się w:
Acta of Bioengineering and Biomechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Robust dual color images watermarking scheme with hyperchaotic encryption based on quaternion DFrAT and genetic algorithm
Autorzy:
Luo, Hui-Xin
Gong, Li-Hua
Chen, Su-Hua
Powiązania:
https://bibliotekanauki.pl/articles/27310094.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
color image watermarking
hyperchaotic encryption
quaternion discrete fractional angular transform
quaternion singular value decomposition
genetic algorithm
Opis:
A robust dual color images watermarking algorithm is designed based on quaternion discrete fractional angular transform (QDFrAT) and genetic algorithm. To guarantee the watermark security, the original color watermark image is encrypted with a 4D hyperchaotic system. A pure quaternion matrix is acquired by performing the discrete wavelet transform (DWT), the block division and the discrete cosine transform on the original color cover image. The quaternion matrix is operated by the QDFrAT to improve the robustness and the security of the watermarking scheme with the optimal transform angle and the fractional order. Then the singular value matrix is obtained by the quaternion singular value decomposition (QSVD) to further enhance the scheme’s stability. The encryption watermark is also processed by DWT and QSVD. Afterward, the singular value matrix of the encryption watermark is embedded into the singular value matrix of the host image by the optimal scaling factor. Moreover, the values to balance imperceptibility and robustness are optimized with a genetic algorithm. It is shown that the proposed color image watermarking scheme performs well in imperceptibility, security, robustness and embedding capacity.
Źródło:
Optica Applicata; 2023, 53, 2; 261--280
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-12 z 12

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