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Wyszukujesz frazę "hybrid optimization method" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
A hybrid algorithm for solving inverse problems in elasticity
Autorzy:
Barabasz, B.
Gajda-Zagórska, E.
Migórski, S.
Paszyński, M.
Schaefer, R.
Smołka, M.
Powiązania:
https://bibliotekanauki.pl/articles/331427.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
inverse problem
hierarchic genetic strategy
hybrid optimization
automatic hp adaptive finite element method
zagadnienie odwrotne
strategia genetyczna
optymalizacja hybrydowa
metoda elementów skończonych
Opis:
The paper offers a new approach to handling difficult parametric inverse problems in elasticity and thermo-elasticity, formulated as global optimization ones. The proposed strategy is composed of two phases. In the first, global phase, the stochastic hp-HGS algorithm recognizes the basins of attraction of various objective minima. In the second phase, the local objective minimizers are closer approached by steepest descent processes executed singly in each basin of attraction. The proposed complex strategy is especially dedicated to ill-posed problems with multimodal objective functionals. The strategy offers comparatively low computational and memory costs resulting from a double-adaptive technique in both forward and inverse problem domains. We provide a result on the Lipschitz continuity of the objective functional composed of the elastic energy and the boundary displacement misfits with respect to the unknown constitutive parameters. It allows common scaling of the accuracy of solving forward and inverse problems, which is the core of the introduced double-adaptive technique. The capability of the proposed method of finding multiple solutions is illustrated by a computational example which consists in restoring all feasible Young modulus distributions minimizing an objective functional in a 3D domain of a photo polymer template obtained during step and flash imprint lithography.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 4; 865-886
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An agent-oriented hierarchic strategy for solving inverse problems
Autorzy:
Smołka, M.
Schaefer, R.
Paszyński, M.
Pardo, D.
Álvarez-Aramberri, J.
Powiązania:
https://bibliotekanauki.pl/articles/329764.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
inverse problem
hybrid optimization method
memetic algorithm
multi-agent system
magnetotelluric data inversion
zadanie odwrotne
optymalizacja hybrydowa
algorytm memetyczny
system wieloagentowy
Opis:
The paper discusses the complex, agent-oriented hierarchic memetic strategy (HMS) dedicated to solving inverse parametric problems. The strategy goes beyond the idea of two-phase global optimization algorithms. The global search performed by a tree of dependent demes is dynamically alternated with local, steepest descent searches. The strategy offers exceptionally low computational costs, mainly because the direct solver accuracy (performed by the hp-adaptive finite element method) is dynamically adjusted for each inverse search step. The computational cost is further decreased by the strategy employed for solution inter-processing and fitness deterioration. The HMS efficiency is compared with the results of a standard evolutionary technique, as well as with the multi-start strategy on benchmarks that exhibit typical inverse problems’ difficulties. Finally, an HMS application to a real-life engineering problem leading to the identification of oil deposits by inverting magnetotelluric measurements is presented. The HMS applicability to the inversion of magnetotelluric data is also mathematically verified.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2015, 25, 3; 483-498
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Cellular particle swarm optimization with a simple adaptive local search strategy for the permutation flow shop scheduling problem
Autorzy:
Seck-Tuoh-Mora, Juan C.
Medina-Marin, Joselito
Martinez-Gomez, Erick S.
Hernandez-Gress, Eva S.
Hernandez-Romero, Norberto
Volpi-Leon, Valeria
Powiązania:
https://bibliotekanauki.pl/articles/230060.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
flow shop
particle swarm optimization (PSO)
local search strategy
hybrid search method
cellular automata
scheduling
Opis:
Permutation flow shop scheduling problem deals with the production planning of a number of jobs processed by a set of machines in the same order. Several metaheuristics have been proposed for minimizing the makespan of this problem. Taking as basis the previous Alternate Two-Phase PSO (ATPPSO) method and the neighborhood concepts of the Cellular PSO algorithm proposed for continuous problems, this paper proposes the improvement of ATPPSO with a simple adaptive local search strategy (called CAPSO-SALS) to enhance its performance. CAPSO-SALS keeps the simplicity of ATPPSO and boosts the local search based on a neighborhood for every solution. Neighbors are produced by interchanges or insertions of jobs which are selected by a linear roulette scheme depending of the makespan of the best personal positions. The performance of CAPSO-SALS is evaluated using the 12 different sets of Taillard’s benchmark problems and then is contrasted with the original and another previous enhancement of the ATPPSO algorithm. Finally, CAPSO-SALS is compared as well with other ten classic and state-of-art metaheuristics, obtaining satisfactory results.
Źródło:
Archives of Control Sciences; 2019, 29, 2; 205-226
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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