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Wyszukujesz frazę "parallel evolutionary algorithms" wg kryterium: Temat


Wyświetlanie 1-7 z 7
Tytuł:
Evolutionary algorithms for job-shop scheduling
Autorzy:
Mesghouni, K.
Hammadi, S.
Borne, P.
Powiązania:
https://bibliotekanauki.pl/articles/907245.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
harmonogramowanie produkcji
algorytm ewolucyjny
reprezentacja równoległa
job-shop scheduling
evolutionary algorithms
parallel representation
Opis:
This paper explains how to use Evolutionary Algorithms (EA) to deal with a flexible job shop scheduling problem, especially minimizing the makespan. The Job-shop Scheduling Problem (JSP) is one of the most difficult problems, as it is classified as an NP-complete one (Carlier and Chretienne, 1988; Garey and Johnson, 1979). In many cases, the combination of goals and resources exponentially increases the search space, and thus the generation of consistently good scheduling is particularly difficult because we have a very large combinatorial search space and precedence constraints between operations. Exact methods such as the branch and bound method and dynamic programming take considerable computing time if an optimum solution exists. In order to overcome this difficulty, it is more sensible to obtain a good solution near the optimal one. Stochastic search techniques such as evolutionary algorithms can be used to find a good solution. They have been successfully used in combinatorial optimization, e.g. in wire routing, transportation problems, scheduling problems, etc. (Banzhaf et al., 1998; Dasgupta and Michalewicz, 1997). Our objective is to establish a practical relationship between the development in the EA area and the reality of a production JSP by developing, on the one hand, two effective genetic encodings, such as parallel job and parallel machine representations of the chromosome, and on the other, genetic operators associated with these representations. In this article we deal with the problem of flexible job-shop scheduling which presents two difficulties: the first is the assignment of each operation to a machine, and the other is the scheduling of this set of operations in order to minimize our criterion (e.g. the makespan).
Źródło:
International Journal of Applied Mathematics and Computer Science; 2004, 14, 1; 91-103
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
GPU-based tuning of quantum-inspired genetic algorithm for a combinatorial optimization problem
Autorzy:
Nowotniak, R.
Kucharski, J.
Powiązania:
https://bibliotekanauki.pl/articles/201268.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
quantum-inspired genetic algorithm
evolutionary computing
meta-optimization
parallel algorithms
GPGPU
Opis:
This paper concerns efficient parameters tuning (meta-optimization) of a state-of-the-art metaheuristic, Quantum-Inspired Genetic Algorithm (QIGA), in a GPU-based massively parallel computing environment (NVidia CUDATMtechnology). A novel approach to parallel implementation of the algorithm has been presented. In a block of threads, each thread transforms a separate quantum individual or different quantum gene; In each block, a separate experiment with different population is conducted. The computations have been distributed to eight GPU devices, and over 400× speedup has been gained in comparison to Intel Core i7 2.93GHz CPU. This approach allows efficient meta-optimization of the algorithm parameters. Two criteria for the meta-optimization of the rotation angles in quantum genes state space have been considered. Performance comparison has been performed on combinatorial optimization (knapsack problem), and it has been presented that the tuned algorithm is superior to Simple Genetic Algorithm and to original QIGA algorithm.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 2; 323-330
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Parallel and distributed implementation of optimization algorithms in fe analyses
Implementacja optymalizacyjnych algorytmów równoległych i rozproszonych w analizie MES
Autorzy:
Handrik, M.
Vasko, M.
Kopas, P.
Powiązania:
https://bibliotekanauki.pl/articles/196393.pdf
Data publikacji:
2012
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
FEM analysis
optimization
parallel computing
distributed computing
BOINC
grid computing
evolutionary algorithms
analiza MES
optymalizacja
obliczenia równoległe
obliczenia rozproszone
rozproszony model obliczeniowy
algorytm ewolucyjny
Opis:
The aim of this paper is analysis of optimization algorithms in terms of their possible solutions in parallelization and distributed computing systems. Main goal is using of evolutionary algorithms and implementation of parallel algorithms. As the software platform for application of distributed optimization algorithms is using software package BOINC. For evaluation of the objective function is used FEM program ADINA.
Artykuł analizuje algorytmy optymalizacyjne pod kątem ich możliwości obliczeń równoległych oraz rozproszonych systemów obliczeniowych. Ukierunkowany jest przede wszystkim na algorytmy ewolucyjne oraz ich implementację równoległą. Jako platforma softwarowa do zastosowania rozproszonego systemu obliczeniowego algorytmu zostało zastosowane oprogramowanie pośredniczące BOINC. W celu oceny funkcji docelowej został zastosowany w MES program ADINA.
Źródło:
Zeszyty Naukowe. Transport / Politechnika Śląska; 2012, 76; 67-74
0209-3324
2450-1549
Pojawia się w:
Zeszyty Naukowe. Transport / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The island model as a Markov dynamic system
Autorzy:
Schaefer, R.
Byrski, A.
Smołka, M.
Powiązania:
https://bibliotekanauki.pl/articles/331253.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
algorytm genetyczny
analiza asymptotyczna
optymalizacja globalna
algorytm ewolucyjny równoległy
łańcuch Markova
genetic algorithms
asymptotic analysis
global optimization
parallel evolutionary algorithms
Markov chain modeling
Opis:
Parallel multi-deme genetic algorithms are especially advantageous because they allow reducing the time of computations and can perform a much broader search than single-population ones. However, their formal analysis does not seem to have been studied exhaustively enough. In this paper we propose a mathematical framework describing a wide class of island-like strategies as a stationary Markov chain. Our approach uses extensively the modeling principles introduced by Vose, Rudolph and their collaborators. An original and crucial feature of the framework we propose is the mechanism of inter-deme agent operation synchronization. It is important from both a practical and a theoretical point of view. We show that under a mild assumption the resulting Markov chain is ergodic and the sequence of the related sampling measures converges to some invariant measure. The asymptotic guarantee of success is also obtained as a simple issue of ergodicity. Moreover, if the cardinality of each island population grows to infinity, then the sequence of the limit invariant measures contains a weakly convergent subsequence. The formal description of the island model obtained for the case of solving a single-objective problem can also be extended to the multi-objective case.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 971-984
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Asymptotic guarantee of success for multi-agent memetic systems
Autorzy:
Byrski, A.
Schaefer, R.
Smołka, M.
Cotta, C.
Powiązania:
https://bibliotekanauki.pl/articles/201942.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
computational multi-agent systems
asymptotic analysis
global optimization
parallel evolutionary algorithms
Markov chain modeling
Opis:
The paper introduces a stochastic model for a class of population-based global optimization meta-heuristics, that generalizes existing models in the following ways. First of all, an individual becomes an active software agent characterized by the constant genotype and the meme that may change during the optimization process. Second, the model embraces the asynchronous processing of agent’s actions. Third, we consider a vast variety of possible actions that include the conventional mixing operations (e.g. mutation, cloning, crossover) as well as migrations among demes and local optimization methods. Despite the fact that the model fits many popular algorithms and strategies (e.g. genetic algorithms with tournament selection) it is mainly devoted to study memetic algorithms. The model is composed of two parts: EMAS architecture (data structures and management strategies) allowing to define the space of states and the framework for stochastic agent actions and the stationary Markov chain described in terms of this architecture. The probability transition function has been obtained and the Markov kernels for sample actions have been computed. The obtained theoretical results are helpful for studying metaheuristics conforming to the EMAS architecture. The designed synchronization allows the safe, coarse-grained parallel implementation and its effective, sub-optimal scheduling in a distributed computer environment. The proved strong ergodicity of the finite state Markov chain results in the asymptotic stochastic guarantee of success, which in turn imposes the liveness of a studied metaheuristic. The Markov chain delivers the sampling measure at an arbitrary step of computations, which allows further asymptotic studies, e.g. on various kinds of the stochastic convergence.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 1; 257-278
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An efficient approach for view selection for data warehouse using tree mining and evolutionary computation
Autorzy:
Thakare, A.
Deshpande, P.
Powiązania:
https://bibliotekanauki.pl/articles/305413.pdf
Data publikacji:
2018
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
database management systems
data warehousing and data mining
query optimization
graph mining
algorithms for parallel computing
evolutionary computations
genetic algorithms
Opis:
The selection of a proper set of views to materialize plays an important role in database performance. There are many methods of view selection that use different techniques and frameworks to select an efficient set of views for materialization. In this paper, we present a new efficient scalable method for view selection under the given storage constraints using a tree mining approach and evolutionary optimization. The tree mining algorithm is designed to determine the exact frequency of (sub)queries in the historical SQL dataset. The Query Cost model achieves the objective of maximizing the performance benefits from the final view set that is derived from the frequent view set given by the tree mining algorithm. The performance benefit of a query is defined as a function of query frequency, query creation cost, and query maintenance cost. The experimental results show that the proposed method is successful in recommending a solution that is fairly close to an optimal solution.
Źródło:
Computer Science; 2018, 19 (4); 431-455
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Solution of linear and non-linear boundary value problems using population-distributed parallel differential evolution
Autorzy:
Nasim, Amnah
Burattini, Laura
Fateh, Muhammad Faisal
Zameer, Aneela
Powiązania:
https://bibliotekanauki.pl/articles/91569.pdf
Data publikacji:
2019
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
parallel evolutionary algorithms
differential evolution
boundary value problems
optimization
Opis:
Cases where the derivative of a boundary value problem does not exist or is constantly changing, traditional derivative can easily get stuck in the local optima or does not factually represent a constantly changing solution. Hence the need for evolutionary algorithms becomes evident. However, evolutionary algorithms are compute-intensive since they scan the entire solution space for an optimal solution. Larger populations and smaller step sizes allow for improved quality solution but results in an increase in the complexity of the optimization process. In this research a population-distributed implementation for differential evolution algorithm is presented for solving systems of 2nd-order, 2-point boundary value problems (BVPs). In this technique, the system is formulated as an optimization problem by the direct minimization of the overall individual residual error subject to the given constraint boundary conditions and is then solved using differential evolution in the sense that each of the derivatives is replaced by an appropriate difference quotient approximation. Four benchmark BVPs are solved using the proposed parallel framework for differential evolution to observe the speedup in the execution time. Meanwhile, the statistical analysis is provided to discover the effect of parametric changes such as an increase in population individuals and nodes representing features on the quality and behavior of the solutions found by differential evolution. The numerical results demonstrate that the algorithm is quite accurate and efficient for solving 2nd-order, 2-point BVPs.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 3; 205-218
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-7 z 7

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