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    Wyświetlanie 1-12 z 12
    Tytuł:
    Analiza efektywności wybranych równoległych implementacji algorytmu Gaussa-Seidela
    Efficiency Analysis of Some Parallel Implementations of the Gauss-Seidel Algorithm
    Autorzy:
    Machaczek, M.
    Sadecki, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/275138.pdf
    Data publikacji:
    2015
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    algorytmy optymalizacji
    obliczenia równoległe
    równoległe algorytmy optymalizacji
    optimization algorithms
    parallel computation
    parallel optimization algorithms
    Opis:
    W artykule przedstawiono analizę porównawczą dotyczącą badania efektywności kilku równoległych implementacji algorytmu Gaussa-Seidela. Analizowany w artykule algorytm pozwala na osiągnięcie dosyć dobrych pod względem szybkości zbieżności oraz wartości współczynnika przyspieszenia obliczeń wyników w porównaniu do standardowej sekwencyjnej oraz równoległej implementacji metody Gaussa-Seidela. Obliczenia praktyczne przeprowadzono w środowisku procesorów wielordzeniowych oraz w środowisku klastrów obliczeniowych.
    The paper presents the results of the efficiency analysis of some parallel implementations of Gauss-Seidel algorithm. The main idea of the presented method consists in successive modification of the search directions used in the computations. This modification is performed on the basis of solutions of local optimization subproblems received for all stages of the algorithm. The analyzed algorithm enable to achieve a good efficiency of parallel computation in terms of speed of convergence and value of speedup factor in comparison to standard sequential and parallel implementation of Gauss-Seidel method. Parallel computation were implemented in the multicore processor and multiprocessor cluster.
    Źródło:
    Pomiary Automatyka Robotyka; 2015, 19, 1; 29-36
    1427-9126
    Pojawia się w:
    Pomiary Automatyka Robotyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Parallel Dynamic Programming Algorithms : Multitransputer Systems
    Autorzy:
    Sadecki, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/907983.pdf
    Data publikacji:
    2002
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    automatyka
    dynamic programming
    parallel computations
    transputers
    multitransputer systems
    parallel optimization algorithms
    Opis:
    The present paper discusses real parallel computations. On the basis of a selected group of dynamic programming algorithms, a number of factors affecting the efficiency of parallel computations such as, e.g., the way of distributing tasks, the interconnection structure between particular elements of the parallel system or the way of organizing of interprocessor communication are analyzed. Computations were implemented in the parallel multitransputer SUPER NODE 1000 system using from 5 to 50 transputers.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2002, 12, 2; 241-255
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An Efficiency Analysis of the Parallel Multitransputer Implementation of Two-Level Optimization Algorithms
    Autorzy:
    Sadecki, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908188.pdf
    Data publikacji:
    2003
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    automatyka
    robotyka
    multitransputer systems
    parallel optimization algorithms
    two-level optimization methods
    Opis:
    The paper presents an approach to improve the efficiency of some two-level optimization algorithms by their implementation in parallel MIMD multiprocessor systems. Diagonal decomposition dynamic programming and parametric optimization methods are considered, and some concepts of their parallelization are discussed. Results regarding the implementation of computations in a parallel multitransputer system are presented. For the analysed problems, the obtained values of speedup are close to the theoretical maximum values.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2003, 13, 2; 205-214
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Benchmarking Procedures for Continuous Optimization Algorithms
    Autorzy:
    Opara, K.
    Arabas, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/308400.pdf
    Data publikacji:
    2011
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    black-box optimization
    comparing optimization algorithms
    evaluation criteria
    parallel computing
    Opis:
    Reliable comparison of optimization algorithms requires the use of specialized benchmarking procedures. This paper highlights motivations which influence their structure, discusses evaluation criteria of algorithms, typical ways of presenting and interpreting results as well as related statistical procedures. Discussions are based on examples from CEC and BBOB benchmarks. Moreover, attention is drawn to these features of comparison procedures, which make them susceptible to manipulation. In particular, novel application of the weak axiom of revealed preferences to the field of benchmarking shows why it may be misleading to assess algorithms on basis of their ranks for each of test problems. Additionally, an idea is presented of developing massively parallel implementation of benchmarks. Not only would this provide faster computation but also open the door to improving reliability of benchmarking procedures and promoting research into parallel implementations of optimization algorithms.
    Źródło:
    Journal of Telecommunications and Information Technology; 2011, 4; 73-80
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    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
    Tytuł:
    Toward the best combination of optimization with fuzzy systems to obtain the best solution for the GA and PSO algorithms using parallel processing
    Autorzy:
    Valdez, Fevrier
    Kawano, Yunkio
    Melin, Patricia
    Powiązania:
    https://bibliotekanauki.pl/articles/384329.pdf
    Data publikacji:
    2020
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    genetic algorithms
    particle swarm optimization (PSO)
    fuzzy logic
    parallel processing
    Opis:
    In general, this paper focuses on finding the best configuration for PSO and GA, using the different migration blocks, as well as the different sets of the fuzzy systems rules. To achieve this goal, two optimization algorithms were configured in parallel to be able to integrate a migration block that allow us to generate diversity within the subpopulations used in each algorithm, which are: the particle swarm optimization (PSO) and the genetic algorithm (GA). Dynamic parameter adjustment was also performed with a fuzzy system for the parameters within the PSO algorithm, which are the following: cognitive, social and inertial weight parameter. In the GA case, only the crossover parameter was modified.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 1; 55-64
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    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:
    Advances in parallel heterogeneous genetic algorithms for continuous optimization
    Autorzy:
    Alba, E.
    Luna, F.
    Nebro, A. J.
    Powiązania:
    https://bibliotekanauki.pl/articles/907622.pdf
    Data publikacji:
    2004
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    algorytm genetyczny
    optymalizacja ciągła
    konwergencja przedwczesna
    parallel genetic algorithms
    continuous optimization
    premature convergence
    heterogeneity
    Opis:
    In this paper we address an extension of a very efficient genetic algorithm (GA) known as Hy3, a physical parallelization of the gradual distributed real-coded GA (GD-RCGA). This search model relies on a set of eight subpopulations residing in a cube topology having two faces for promoting exploration and exploitation. The resulting technique has been shown to yield very accurate results in continuous optimization by using crossover operators tuned to explore and exploit the solutions inside each subpopulation. We introduce here a further extension of Hy3, called Hy4, that uses 16 islands arranged in a hypercube of four dimensions. Thus, two new faces with different exploration/exploitation search capabilities are added to the search performed by Hy3. We analyze the importance of running a synchronous versus an asynchronous version of the models considered. The results indicate that the proposed Hy4 model overcomes the Hy3 performance because of its improved balance between exploration and exploitation that enhances the search. Finally, we also show that the async Hy4 model scales better than the sync one.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 317-333
    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:
    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:
    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
      Wyświetlanie 1-12 z 12

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