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Wyszukujesz frazę "genetic optimization algorithm" wg kryterium: Temat

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    Wyświetlanie 1-5 z 5
    Tytuł:
    Performance investigation and element optimization of 2D array transducer using Bat Algorithm
    Autorzy:
    Tantawy, Dina Mohamed
    Eladawy, Mohamed
    Hassan, Mohamed Alimaher
    Mubarak, Roaa
    Powiązania:
    https://bibliotekanauki.pl/articles/140685.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    2D ultrasound arrays
    Binary Bat Algorithm
    Genetic Algorithm
    Optimization
    Opis:
    One of the least expensive and safest diagnostic modalities routinely used is ultrasound imaging. An attractive development in this field is a two-dimensional (2D) matrix probe with three-dimensional (3D) imaging. The main problems to implement this probe come from a large number of elements they need to use. When the number of elements is reduced the side lobes arising from the transducer change along with the grating lobes that are linked to the periodic disposition of the elements. The grating lobes are reduced by placing the elements without any consideration of the grid. In this study, the Binary Bat Algorithm (BBA) is used to optimize the number of active elements in order to lower the side lobe level. The results are compared to other optimization methods to validate the proposed algorithm.
    Źródło:
    Archives of Electrical Engineering; 2020, 69, 3; 561-579
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of electric and magnetic field intensities in proximity of power lines using genetic and particle swarm algorithms
    Autorzy:
    Król, K.
    Machczyński, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/141588.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    power line
    electric field
    magnetic field
    optimization
    genetic algorithm
    particle swarm algorithm
    Opis:
    The paper presents optimization of power line geometrical parameters aimed to reduce the intensity of the electric field and magnetic field intensity under an overhead power line with the use of a genetic algorithm (AG) and particle swarm optimization (PSO). The variation of charge distribution along the conductors as well as the sag of the overhead line and induced currents in earth wires were taken into account. The conductor sag was approximated by a chain curve. The charge simulation method (CSM) and the method of images were used in the simulations of an electric field, while a magnetic field were calculated using the Biot–Savart law. Sample calculations in a three-dimensional system were made for a 220 kV single – circuit power line. A comparison of the used optimization algorithms was made.
    Źródło:
    Archives of Electrical Engineering; 2018, 67, 4; 829-843
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multi-objective design optimization of five-phase fractional-slot concentrated-winding surface-mounted permanent-magnet machine
    Autorzy:
    Nekoubin, Amir
    Soltani, Jafar
    Dowlatshah, Milad
    Powiązania:
    https://bibliotekanauki.pl/articles/949883.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Finite Element Method
    genetic algorithm
    optimization
    permanent-magnet motors
    Opis:
    The multi-phase permanent-magnet machines with a fractional-slot concentratedwinding (FSCW) are a suitable choice for certain purposes like aircraft, marine, and electric vehicles, because of the fault tolerance and high power density capability. The paper aims to design, optimize and prototype a five-phase fractional-slot concentrated-winding surface-mounted permanent-magnet motor. To optimize the designed multi-phase motor a multi-objective optimization technique based on the genetic algorithm method is applied. The machine design objectives are to maximize torque density of the motor and maximize efficiency then to determine the best choice of the designed machine parameters. Then, the two-dimensional Finite Element Method (2D-FEM) is employed to verify the performance of the optimized machine. Finally, the optimized machine is prototyped. The paper found that the results of the prototyped machine validate the results of theatrical analyses of the machine and accurate consideration of the parameters improved the acting of the machine.
    Źródło:
    Archives of Electrical Engineering; 2020, 69, 4; 873-889
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation
    Autorzy:
    Pandiarajan, K.
    Babulal, C. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/141059.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    non-dominated sorting genetic algorithm
    generation rescheduling
    particle swarm optimization (PSO)
    differential evolution
    overload index
    Opis:
    This paper presents an effective method of network overload management in power systems. The three competing objectives 1) generation cost 2) transmission line overload and 3) real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem
    Źródło:
    Archives of Electrical Engineering; 2014, 63, 3; 367-384
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An adaptive particle swarm optimization algorithm for robust trajectory tracking of a class of under actuated system
    Autorzy:
    Kumar, V. E.
    Jerome, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/141105.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    inverted pendulum
    LQR controller
    particle swarm optimization (PSO)
    genetic algorithm
    adaptive inertia weight factor
    state feedback control
    Opis:
    This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via trial and error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, and the experimental results of APSO are compared with that of the conventional PSO and GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed and precision of PSO in obtaining the robust trajectory tracking of inverted pendulum.
    Źródło:
    Archives of Electrical Engineering; 2014, 63, 3; 345-365
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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