Wyniki wyszukiwania - Biblioteka Nauki

1

Optimization of Square-shaped Bolted Joints Based on Improved Particle Swarm Optimization Algorithm

100%

Chen K. , Yang C. , Zhao Y. , Niu P. , Niu N. , Wu H.

Eksploatacja i Niezawodność

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2023

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tom Vol. 25, no. 3

art. no. 168487

EN

The bolted joint is widely used in heavy-duty CNC machine tools, which has huge influence on working precision and overall stiffness of CNC machine. The process parameters of group bolt assembly directly affect the stiffness of the connected parts. The dynamic model of bolted joints is established based on the fractal theory, and the overall stiffness of joint surface is calculated. In order to improve the total stiffness of bolted assembly, an improved particle swarm optimization algorithm with combination of time-varying weights and contraction factor is proposed. The input parameters are preloading of bolts, fractal dimension, roughness, and object thickness. The main goal is to maximize the global rigidity. The optimization results show that improved algorithm has better convergence, faster calculation speed, preferable results, and higher optimization performance than standard particle swarm optimization algorithm. Moreover, the global rigidity optimization is achieved.

2

Wykorzystanie algorytmu optymalizacji rojem cząstek do rozwiązywania układów równań nieliniowych

75%

Prus K. , Figielska E.

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

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2023

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tom nr 28

27--40

PL

Artykuł dotyczy wykorzystania algorytmu optymalizacji rojem cząstek do rozwiązywania układów równań nieliniowych. Przeprowadzona została eksperymentalna analiza efektywności i skuteczności działania algorytmu w zależności od ustawień jego parametrów.

EN

The article concerns the use of a particle swarm optimization algorithm for solving nonlinear equation systems. An experimental analysis of the effectiveness and efficiency of the algorithm has been conducted, considering various settings of its parameters.

3

Estymacja rocznych mocy szczytowych transformatorów SN/nn przy użyciu algorytmów PSO

75%

Baczyński D.

Przegląd Elektrotechniczny

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2012

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tom R. 88, nr 8

96-98

PL

W artykule przedstawiony został sposób budowania zależności estymacyjnej przy użyciu algorytmu optymalizacji rojem cząstek (PSO). Algorytm ten został wykorzystany do dobrania parametrów formuły estymacyjnej. Wykorzystuje ona informacje o energii elektrycznej zużytej przez poszczególne grupy odbiorców. Wyniki testów zostały porównane z wynikami uzyskanymi przy użyciu algorytmów ewolucyjnych. Na tej podstawie sformułowano ogólne wnioski dotyczące wykorzystania algorytmów PSO do budowania modeli matematycznych różnych zjawisk.

EN

Building of estimation formula using particle swarm optimization algorithm (PSO) is presented in the paper. This algorithm was used for finding of estimation formula parameters. It uses information about energy consumed by particular receiver’s groups. Test results were compared with those obtained using evolutionary algorithms. On these basis general conclusions concerning PSO usage for constructing of various phenomena models were formulated.

4

Novel Speed Sensorless DTC Design for a Five-Phase Induction Motor with an Intelligent Fractional Order Controller Based-MRAS Estimator

75%

Guedida S. , Tabbache B. , Benzaoui K. M. S. , Nounou K. , Nesri M.

Power Electronics and Drives

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2024

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tom Vol. 9 (44)

63--85

EN

This paper presents a fractional-order adaptive mechanism-based model reference adaptive system (MRAS) configuration for speed estimation of sensorless direct torque control (DTC) of a five-phase induction motor. In effect, the fractional-order proportional-integral (FOPI) controller parameters are obtained by the particle swarm optimisation (PSO) algorithm to enhance the MRAS observer response. Thus, the developed algorithm in the speed loop control of the DTC strategy to increase its robustness against disturbances. Moreover, a comparative study has been done of the proposed MRAS-PSO/FOPI speed estimator with the conventional MRAS-proportional-integral (PI) and the PSO-based MRAS-PI. Simulation results have carried out of the different controllers used in the adaptation mechanism of the MRAS estimator, to show the performance and robustness of the proposed MRAS-PSO/FOPI algorithm in use.

5

New weighted coefficients of the average-derivative modeling method based on global optimization algorithms

63%

He C. , Chen J.-B.

Acta Geophysica

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2024

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tom Vol. 72, no. 2

619--636

EN

The average-derivative optimal method (ADM) is widely applied in frequency-domain forward modeling for its high accuracy and simplicity. Since tuning weighted coefficients can suppress the numerical dispersion, it is extremely important to adopt a suitable optimization algorithm to determine the ADM coefficients. To date, most schemes associated with the ADM have adopted the conventional local optimization algorithms, which are sensitive to the initial value and easy to converge on local optimum. The motivation of this paper is to derive new and more accurate ADM coefficients for 2D frequency-domain elastic-wave equation by the global optimization algorithms, which can escape from the local optimum with a certain probability. We adopt simulated annealing (SA) and particle swarm optimization (PSO) algorithms for global optimization and numerical modeling. Compared with the conventional local optimization algorithm, the global optimization algorithms have smaller phase errors, especially for S-wave phase velocity. Numerical examples demonstrate that the global optimization algorithms produce more accurate results than the local optimization algorithm.

6

Predicting and minimizing the blasting cost in limestone mines using a combination of gene expression programming and particle swarm optimization

63%

Bastami R. , Bazzazi A. A. , Shoormasti H. H. , Ahangari K.

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tom Vol. 65, no. 4

835--850

EN

Blasting cost prediction and optimization is of great importance and significance to achieve optimal fragmentation through controlling the adverse consequences of the blasting process. By gathering explosive data from six limestone mines in Iran, the present study aimed to develop a model to predict blasting cost, by gene expression programming method. The model presented a higher correlation coefficient (0.933) and a lower root mean square error (1088) comparing to the linear and nonlinear multivariate regression models. Based on the sensitivity analysis, spacing and ANFO value had the most and least impact on blasting cost, respectively. In addition to achieving blasting cost equation, the constraints such as frag-mentation, fly rock, and back break were considered and analyzed by the gene expression programming method for blasting cost optimization. The results showed that the ANFO value was 9634 kg, hole dia-meter 76 mm, hole number 398, hole length 8.8 m, burden 2.8 m, spacing 3.4 m, hardness 3 Mhos, and uniaxial compressive strength 530 kg/cm2 as the blast design parameters, and blasting cost was obtainedas 6072 Rials/ton, by taking into account all the constraints. Compared to the lowest blasting cost among the 146-research data (7157 Rials/ton), this cost led to a 15.2% reduction in the blasting cost and optimal control of the adverse consequences of the blasting process.

7

Optymalizacja procesu wiercenia otworów w elektronicznych płytach drukowanych przy użyciu algorytmu roju cząstek

63%

Słowik A. , Popławski M.

Przegląd Elektrotechniczny

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2016

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tom R. 92, nr 8

10--13

PL

W niniejszej pracy przedstawiono optymalizację procesu wiercenia otworów w elektronicznych płytach drukowanych. Do realizacji tego zadania zastosowano algorytm optymalizacji rojem cząstek w wersji dostosowanej do optymalizacji problemów kombinatorycznych. Opracowany algorytm przetestowano przy użyciu ogólnie dostępnych danych benchmarkowych z biblioteki VLSI Data Set. Biblioteka ta zawiera dane odnośnie przykładowych elektronicznych płyt drukowanych. Otrzymane wyniki porównano z wynikami otrzymanymi przy użyciu standardowego algorytmu rojowego przystosowanego do optymalizacji problemów o dyskretnych dziedzinach. Trasa ramienia wiercącego uzyskana przy użyciu proponowanego algorytmu jest krótsza od trasy uzyskanej standardowym algorytmem roju dla dziedzin dyskretnych.

EN

In this paper, the optimization of the drilling holes process in the electronic printed circuit boards is presented. The particle swarm optimization algorithm in the version dedicated to the optimization of the combinatorial problems is applied for this task realization. The algorithm elaborated in this paper was tested with the use of global accessible benchmark data sets with the VLSI Data Set library. This library contains the data of the exemplary electronic printed boards. The results obtained using proposed algorithm were compared with the results obtained using standard particle swarm optimization algorithm in the version dedicated for optimization of the problems with discrete domains. The route of drilling arm obtained using proposed algorithm was shorter than the route of drilling arm obtained using standard particle swarm optimization algorithm for discrete domains.

8

Particle swarm optimization of fuzzy fractional PDμ+I controller of a PMDC motor for reliable operation of wire-feeder units of GMAW welding machine

63%

Hamouda N.

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2020

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tom R. 96, nr 12

40--46

EN

In this article, we consider the development of an optimal control approach based on fuzzy fractional PDμ+I controller to improve the speed error-tracking and control capability of a permanent magnet DC Motor (PMDC) driven wire-feeder systems (WFSs) of gas metal arc welding (GMAW) process. The proposed controller employs an optimized fractional-order proportional derivative + integral (PDμ+I) controller that serves to eliminate oscillations, overshoots, undershoots and steady state fluctuations of the PMDC motor and makes the wire-feeder unit (WFU) has fast and stable starting process as well as excellent dynamic characteristics. The fixed controller parameters are meta-heuristically selected via a particle swarm optimization (PSO) algorithm. Numerical simulations are performed in MATLAB/SIMULINK environment and the performance of the proposed fuzzy fractional PDμ+I controller is validated. The simulation tests clearly demonstrate the significant improvement rendered by the proposed fuzzy PDμ+I controller in the wire-feeder system's reference tracking performance, torque disturbance rejection capability and robustness against model uncertainties.

PL

Analizowano optymalne sterowanie silnikiem zDC z magnesami trwałymi wykorzystujące sterownik fuzzy PDμ+I. Silnik stosowany jest do sterowania procesem spawania. Układ sterowania wykorzystuje sterownik proporcjonalny ułamkowego rzędu i całkujący zapewniające dobrą dynamikę układu – bez oscylacji.

9

A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters

51%

Zhou J. , Yang P. , Yong W. , Khandelwal M. , Huang S.

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tom Vol. 72, no. 3

1847--1866

EN

With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during the excavation of hard rock tunnels using tunnel boring machines (TBM) significantly impact construction efficiency and cost-effectiveness. The existing lithology testing methods need to be more efficient in aligning with TBM operational efficiency. In recent years, the rapid advancement of artificial intelligence has paved the way for its integration into numerous domains, including tunnel engineering. To address this issue, this study proposes three innovative hybrid RF-based intelligent models, namely PSO-RF, ALO-RF, and GWO-RF, for the precise prediction of lithology in hard rock tunnels using TBM working parameters. The TBM operating parameters of the Jilin Yinsong Water Supply Project serve as the basis for this investigation. Twelve distinct characteristic parameters relevant to the lithology of the tunnel working face were carefully selected as input parameters for lithology prediction. Comparative analysis of the three hybrid models reveals that GWO-RF demonstrates exceptional lithology prediction performance (ACC = 0.999924; PREA = 0.0.9999976; RECA = 0.999775; F1A = 0.999876; Kappa = 0.999911), whereas PSO-RF and ALO-RF exhibit slightly inferior performance. Nonetheless, all three hybrid models exhibit a significant improvement in prediction accuracy compared to the unoptimized RF model. The research findings presented herein facilitate the swift determination of TBM working surface lithology, enabling timely adjustment of TBM working parameters, reducing equipment wear and tear, and enhancing construction efficiency.