Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "population-based algorithm" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-6 z 6
    Tytuł:
    Gold rush optimizer : a new population-based metaheuristic algorithm
    Autorzy:
    Zolf, Kamran
    Powiązania:
    https://bibliotekanauki.pl/articles/2204102.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    gold rush optimizer
    metaheuristic
    global optimization
    population-based algorithm
    Opis:
    Today’s world is characterised by competitive environments, optimal resource utilization, and cost reduction, which has resulted in an increasing role for metaheuristic algorithms in solving complex modern problems. As a result, this paper introduces the gold rush optimizer (GRO), a population-based metaheuristic algorithm that simulates how gold-seekers prospected for gold during the Gold Rush Era using three key concepts of gold prospecting: migration, collaboration, and panning. The GRO algorithm is compared to twelve well-known metaheuristic algorithms on 29 benchmark test cases to assess the proposed approach’s performance. For scientific evaluation, the Friedman and Wilcoxon signed-rank tests are used. In addition to these test cases, the GRO algorithm is evaluated using three real-world engineering problems. The results indicated that the proposed algorithm was more capable than other algorithms in proposing qualitative and competitive solutions.
    Źródło:
    Operations Research and Decisions; 2023, 33, 1; 113--150
    2081-8858
    2391-6060
    Pojawia się w:
    Operations Research and Decisions
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multi-population-based algorithm with an exchange of training plans based on population evaluation
    Autorzy:
    Łapa, Krystian
    Cpałka, Krzysztof
    Kisiel-Dorohinicki, Marek
    Paszkowski, Józef
    Dębski, Maciej
    Le, Van-Hung
    Powiązania:
    https://bibliotekanauki.pl/articles/2147148.pdf
    Data publikacji:
    2022
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    population-based algorithm
    multi-population algorithm
    hybrid algorithm
    island algorithm
    subpopulation evaluation
    training plan
    Opis:
    Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 4; 239--253
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Evolutionary algorithm with a configurable search mechanism
    Autorzy:
    Łapa, Krystian
    Cpałka, Krzysztof
    Laskowski, Łukasz
    Cader, Andrzej
    Zeng, Zhigang
    Powiązania:
    https://bibliotekanauki.pl/articles/1837536.pdf
    Data publikacji:
    2020
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    evolutionary algorithm
    population-based algorithm
    optimization
    operator pool
    operator selection
    individual selection
    Opis:
    In this paper, we propose a new population-based evolutionary algorithm that automatically configures the used search mechanism during its operation, which consists in choosing for each individual of the population a single evolutionary operator from the pool. The pool of operators comes from various evolutionary algorithms. With this idea, a flexible balance between exploration and exploitation of the problem domain can be achieved. The approach proposed in this paper might offer an inspirational alternative in creating evolutionary algorithms and their modifications. Moreover, different strategies for mutating those parts of individuals that encode the used search operators are also taken into account. The effectiveness of the proposed algorithm has been tested using typical benchmarks used to test evolutionary algorithms.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 3; 151-171
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Parallel PBIL applied to power system controller design
    Autorzy:
    Folly, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/91747.pdf
    Data publikacji:
    2013
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    Population-Based Incremental Learning algorithm
    PBIL algorithm
    Opis:
    Population-Based Incremental Learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning derived from artificial neural networks. PBIL has recently received increasing attention in various engineering fields due to its effectiveness, easy implementation and robustness. Despite these strengths, it was reported in the last few years that PBIL suffers from issues of loss of diversity in the population. To deal with this shortcoming, this paper uses parallel PBIL based on multi-population. In parallel PBIL, two populations are used where both probability vectors (PVs) are initialized to 0.5. It is believed that by introducing two populations, the diversity in the population can be increased and better results can be obtained. The approach is applied to power system controller design. Simulations results show that the parallel PBIL approach performs better than the standard PBIL and is as effective as another diversity increasing PBIL called adaptive PBIL.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 3; 215-223
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On-line signature partitioning using a population based algorithm
    Autorzy:
    Zalasiński, Marcin
    Łapa, Krystian
    Cpałka, Krzysztof
    Przybyszewski, Krzysztof
    Yen, Gary G.
    Powiązania:
    https://bibliotekanauki.pl/articles/91729.pdf
    Data publikacji:
    2020
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    on-line signature
    biometrics
    signature partitioning
    population-based algorithm
    podpis on-line
    biometria
    partycjonowanie sygnatur
    algorytm populacyjny
    Opis:
    The on-line signature is a biometric attribute which can be used for identity verification. It is a very useful characteristic because it is commonly accepted in societies across the world. However, the verification process using this particular biometric feature is a rather difficult one. Researchers working on identity verification involving the on-line signature might face various problems, including the different discriminative power of signature descriptors, the problem of a large number of descriptors, the problem of descriptor generation, etc. However, population-based algorithms (PBAs) can prove very useful when resolving these problems. Hence, we propose a new method for on-line signature partitioning using a PBA in order to improve the verification process effectiveness. Our method uses the Differential Evolution algorithm with a properly defined evaluation function for creating the most characteristic partitions of the dynamic signature. We present simulation results of the proposed method for the BioSecure DS2 database distributed by the BioSecure Association.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 1; 5-13
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Probabilistic model-building algorithms as tool to find optimum of a function
    Algorytmy z modelem probabilistycznym jako narzędzie optymalizacji funkcji
    Autorzy:
    Reichel, A.
    Nowak, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/87296.pdf
    Data publikacji:
    2015
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    algorytm PBIL
    algorytm cGA
    metody heurystyczne
    optymalizacja
    population-based incremental learning
    compact genetic algorithm
    heuristic methods
    optimization
    Opis:
    The aim of this paper is to present the probabilistic modelbuilding heuristics which is a modification of an evolutionary algorithm. the Probabilistic-Based Incremental Learning (PBIL) and the compact Genetic Algorithm (cGA) is presented as a example of the probabilistic model building algorithms dedicated to the binary problems. Both heuristics are tested on three functions that allow to investigate the advantages, disadvantages and limitations of methods under consideration.
    Celem niniejszego artykułu jest przedstawienie heurystyk wieloagentowych wykorzystujących model probabilistyczny. W artykule omówiono dwie metody: the Probabilistic-Based Incremental Learning (PBIL) oraz the compact Genetic Algorithm (cGA), będące przykładami heurystyk z modelem probabilistycznym. Obie metody są przeznaczone do rozwiązywania problemów binarnych. W ramach pracy metody te testowano na trzech funkcjach zdefiniowanych w przestrzeni ciągów binarnych. Testy miały zbadać zalety, wady oraz ograniczenia obu prezentowanych heurystyk populacyjnych.
    Źródło:
    Zeszyty Naukowe. Matematyka Stosowana / Politechnika Śląska; 2015, 5; 79-97
    2084-073X
    Pojawia się w:
    Zeszyty Naukowe. Matematyka Stosowana / Politechnika Śląska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies