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    Wyświetlanie 1-4 z 4
    Tytuł:
    Surrogate synthesis of excitation systems for frame tangential eddy current probes
    Autorzy:
    Halchenko, Volodymir Yakovych
    Trembovetska, Ruslana Volodymyrivna
    Tychkov, Volodymir Volodymyrovych
    Powiązania:
    https://bibliotekanauki.pl/articles/1955174.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    additive neural network regression
    eddy current probe
    stochastic optimization algorithm
    surrogate optimization
    uniform eddy current density distribution
    velocity effect
    addytywna regresja sieci neuronowej
    sonda prądów wirowych
    algorytm optymalizacji stochastycznej
    optymalizacja zastępcza
    równomierny rozkład gęstości prądów wirowych
    efekt prędkości
    Opis:
    Existing scientific studies devoted to the design of eddy-current probes with a priori given configuration of the electromagnetic excitation field, which provide a uniform eddy current density distribution, consider a wide class of such, but are limited to the case when the probe is stationary relative to the testing object. Therefore, the actual problem is the synthesis of moving tangential eddy current probes with a frame excitation system that provides a uniform eddy current density distribution in the testing object, the solution of which is proposed in this study. A mathematical method for nonlinear surrogate synthesis of excitation systems for frame moving tangential surface eddy current probes, which implements a uniform eddy current density distribution of the testing zone object, is proposed. A metamodel of the volumetric structure of the excitation system of the frame tangential eddy current probe, applied in the process of surrogate optimal parametric synthesis, has been created. The examples of nonlinear synthesis of excitation systems using modern metaheuristic stochastic algorithms for finding the global extremum are considered. The numerical results of the obtained solutions of the problems are presented. The efficiency of the synthesized structures of excitation systems in comparison with classical analogs is shown on the graphs of the eddy current density distribution on the object surface in the testing zone.
    Źródło:
    Archives of Electrical Engineering; 2021, 70, 4; 743-757
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Short Introduction to Stochastic Optimization
    Autorzy:
    Ombach, Jerzy
    Powiązania:
    https://bibliotekanauki.pl/articles/1373633.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
    Tematy:
    global optimization
    stochastic algorithm
    random search
    convergence of metaheuristics
    Opis:
    We present some typical algorithms used for finding global minimum/ maximum of a function defined on a compact finite dimensional set, discuss commonly observed procedures for assessing and comparing the algorithms’ performance and quote theoretical results on convergence of a broad class of stochastic algorithms.
    Źródło:
    Schedae Informaticae; 2014, 23; 9-20
    0860-0295
    2083-8476
    Pojawia się w:
    Schedae Informaticae
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Stochastic fractal based multiobjective fruit fly optimization
    Autorzy:
    Zuo, C.
    Wu, L.
    Zeng, Z. F.
    Wei, H. L.
    Powiązania:
    https://bibliotekanauki.pl/articles/330026.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    multiobjective optimization
    fruit fly optimization algorithm
    stochastic fractal
    optymalizacja wielokryterialna
    algorytm optymalizacji
    fraktal stochastyczny
    Opis:
    The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2017, 27, 2; 417-433
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    DSSA+: distributed collision avoidance algorithm in an environment where both course and speed changes are allowed
    Autorzy:
    Hirayama, K.
    Miyake, K.
    Shiotani, T.
    Okimoto, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/116483.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    collision avoidance
    collision avoidance algorithm
    vessel course changes
    vessel speed changes
    Distributed Stochastic Search Algorithm (DSSA)
    colregs
    ARPA
    Distributed Constraint Optimization Problem (DCOP)
    Opis:
    Distributed Stochastic Search Algorithm (DSSA) is one of state-of-the-art distributed algorithms for the ship collision avoidance problem. In DSSA, whenever a ship encounters with any number of other ships (neighboring ships), she will select her course with a minimum cost after coordinating their decisions with her neighboring ships. The original DSSA assumes that ships can change only their courses while keeping their speed considering kinematic properties of ships in general. However, considering future possibilities to address more complex situations that may cause ship collision or to deal with collision of other vehicles (such as mobile robots or drones), the options of speed changes are necessary for DSSA to make itself more flexible and extensive. In this paper, we present DSSA+, as a generalization of DSSA, in which speed change are naturally incorporated as decision variables in the original DSSA. Experimental evaluations are provided to show how powerful this generalization is.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2019, 13, 1; 117-123
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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