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    Wyświetlanie 1-13 z 13
    Tytuł:
    A novel approach for power system stabilizer control parameter selection: a case-study on two-area four-machine system
    Autorzy:
    Gude, Murali Krishna
    Salma, Umme
    Powiązania:
    https://bibliotekanauki.pl/articles/2086725.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    grey wolf optimizer
    power system stabilizer
    optimization
    stability
    Opis:
    This paper proposes a power system stabilizer (PSS) with optimal controller parameters for damping low-frequency power oscillations in the power system. A novel meta-heuristic, weighted grey wolf optimizer (GWO) has been proposed, it is a variant of the grey wolf optimizer (GWO). The proposed WGWO algorithm has been executed in the selection of controller parameters of a PSS in a multi-area power system. A two-area four- machine test system has been considered for the performance evaluation of an optimally tuned PSS. A multi-objective function based on system eigenvalues has been minimized for obtained optimal controller parameters. The damping characteristics and eigenvalue location in the proposed approach have been compared with the other state-of-the-art- methods, which illustrates the effectiveness of the proposed approach.
    Źródło:
    Archives of Electrical Engineering; 2022, 71, 2; 297--407
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Feature optimization using a two-tier hybrid optimizer in an Internet of Things network
    Autorzy:
    Agrawal, Akhileshwar Prasad
    Singh, Nanhay
    Powiązania:
    https://bibliotekanauki.pl/articles/15548024.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    IoT
    Internet of Things
    anomaly mitigation
    GWO
    Gray Wolf Optimizer
    feature optimization
    PSO
    particle swarm optimizer
    Internet Rzeczy
    optymalizacja funkcji
    Opis:
    The growing use of the Internet of Things (IoT) in smart applications necessitates improved security monitoring of IoT components. The security of such components is monitored using intrusion detection systems which run machine learning (ML) algorithms to classify access attempts as anomalous or normal. However, in this case, one of the issues is the large length of the data feature vector that any ML or deep learning technique implemented on resource-constrained intelligent nodes must handle. In this paper, the problem of selecting an optimal-feature set is investigated to reduce the curse of data dimensionality. A two-layered approach is proposed: the first tier makes use of a random forest while the second tier uses a hybrid of gray wolf optimizer (GWO) and the particle swarm optimizer (PSO) with the k-nearest neighbor as the wrapper method. Further, differential weight distribution is made to the local-best and global-best positions in the velocity equation of PSO. A new metric, i.e., the reduced feature to accuracy ratio (RFAR), is introduced for comparing various works. Three data sets, namely, NSLKDD, DS2OS and BoTIoT, are used to evaluate and validate the proposed work. Experiments demonstrate improvements in accuracy up to 99.44%, 99.44% and 99.98% with the length of the optimal-feature vector equal to 9, 4 and 8 for the NSLKDD, DS2OS and BoTIoT data sets, respectively. Furthermore, classification improves for many of the individual classes of attacks: denial-of-service (DoS) (99.75%) and normal (99.52%) for NSLKDD, malicious control (100%) and DoS (68.69%) for DS2OS, and theft (95.65%) for BoTIoT.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 2; 313--326
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Double Layered Priority based Gray Wolf Algorithm (PrGWO-SK) for safety management in IoT network through anomaly detection
    Autorzy:
    Agrawal, Akhileshwar Prasad
    Singh, Nanhay
    Powiązania:
    https://bibliotekanauki.pl/articles/2200943.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    Gray Wolf Optimizer
    anomaly detection
    feature selection
    predictive maintenance
    Opis:
    For mitigating and managing risk failures due to Internet of Things (IoT) attacks, many Machine Learning (ML) and Deep Learning (DL) solutions have been used to detect attacks but mostly suffer from the problem of high dimensionality. The problem is even more acute for resource starved IoT nodes to work with high dimension data. Motivated by this problem, in the present work a priority based Gray Wolf Optimizer is proposed for effectively reducing the input feature vector of the dataset. At each iteration all the wolves leverage the relative importance of their leader wolves’ position vector for updating their own positions. Also, a new inclusive fitness function is hereby proposed which incorporates all the important quality metrics along with the accuracy measure. In a first, SVM is used to initialize the proposed PrGWO population and kNN is used as the fitness wrapper technique. The proposed approach is tested on NSL-KDD, DS2OS and BoTIoT datasets and the best accuracies are found to be 99.60%, 99.71% and 99.97% with number of features as 12,6 and 9 respectively which are better than most of the existing algorithms.
    Źródło:
    Eksploatacja i Niezawodność; 2022, 24, 4; 641--654
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Swarm intelligence algorithm based on competitive predators with dynamic virtual teams
    Autorzy:
    Yang, S.
    Sato, Y.
    Powiązania:
    https://bibliotekanauki.pl/articles/91592.pdf
    Data publikacji:
    2017
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    swarm intelligence
    sitness predator optimizer
    dynamic virtual team
    population diversity
    Opis:
    In our previous work, Fitness Predator Optimizer (FPO) is proposed to avoid premature convergence for multimodal problems. In FPO, all of the particles are seen as predators. Only the competitive, powerful predator that are selected as an elite could achieve the limited opportunity to update. The elite generation with roulette wheel selection could increase individual independence and reduce rapid social collaboration. Experimental results show that FPO is able to provide excellent performance of global exploration and local minima avoidance simultaneously. However, to the higher dimensionality of multimodal problem, the slow convergence speed becomes the bottleneck of FPO. A dynamic team model is utilized in FPO, named DFPO to accelerate the early convergence rate. In this paper, DFPO is more precisely described and its variant, DFPO-r is proposed to improve the performance of DFPO. A method of team size selection is proposed in DFPO-r to increase population diversity. The population diversity is one of the most important factors that determines the performance of the optimization algorithm. A higher degree of population diversity is able to help DFPO-r alleviate a premature convergence. The strategy of selection is to choose team size according to the higher degree of population diversity. Ten well-known multimodal benchmark functions are used to evaluate the solution capability of DFPO and DFPO-r. Six benchmark functions are extensively set to 100 dimensions to investigate the performance of DFPO and DFPO-r compared with LBest PSO, Dolphin Partner Optimization and FPO. Experimental results show that both DFPO and DFPO-r could demonstrate the desirable performance. Furthermore, DFPO-r shows better robustness performance compared with DFPO in experimental study.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 2; 87-101
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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:
    Umiejętność podejmowania decyzji przez uczniów, jako warunek ich efektywnego funkcjonowania
    Decision Making Skill by Pupils as a Condition to Their Effective Functioning
    Autorzy:
    WILSZ, JOLANTA
    Powiązania:
    https://bibliotekanauki.pl/articles/456526.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Rzeszowski
    Tematy:
    decyzje
    postulator
    optymalizator
    realizator
    system sterujący
    decisions
    optimizer
    performer
    steering system
    Opis:
    W artykule omówiono rodzaje problemów do rozwiązania. Przedstawiono działalność decy-zyjną opartą na strukturze systemu sterującego. Omówiono relacje zachodzące między podsystemami tego systemu: postulatorem, optymalizatorem i realizatorem. Uzasadniono konieczność zapoznania uczniów z problematyką dotyczącą podejmowania decyzji w ujęciu cybernetycznym.
    In the article the kinds of problems to be solved were discussed. Decision making based on a structure of a steering system was presented. The relations occurring between subsystems of that system such as: postulator, optimizer and performer. The necessity of making pupils familiar with problems connected with the issue of decision making according to the cybernetic approach, was given grounds to.
    Źródło:
    Edukacja-Technika-Informatyka; 2018, 9, 3; 172-177
    2080-9069
    Pojawia się w:
    Edukacja-Technika-Informatyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm
    Autorzy:
    Shial, Gyanaranjan
    Saho, Sabita
    Panigrahi, Sibarama
    Powiązania:
    https://bibliotekanauki.pl/articles/27312857.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    grey wolf optimizer
    JAYA algorithm
    article swarm optimization
    ine-cosinealgorithm
    partitional clustering
    Opis:
    This paper presents a hybrid meta-heuristic algorithm that uses the grey wolfoptimization (GWO) and the JAYA algorithm for data clustering. The ideais to use the explorative capability of the JAYA algorithm in the exploitativephase of GWO to form compact clusters. Here, instead of using only one bestand one worst solution for generating offspring, the three best wolves (alpha,beta and delta) and three worst wolves of the population are used. So, the bestand worst wolves assist in moving towards the most feasible solutions and simul-taneously it helps to avoid from worst solutions; this enhances the chances oftrapping at local optimal solutions. The superiority of the proposed algorithmis compared with five promising algorithms; namely, the sine-cosine (SCA),GWO, JAYA, particle swarm optimization (PSO), and k-means algorithms.The performance of the proposed algorithm is evaluated for 23 benchmarkmathematical problems using the Friedman and Nemenyi hypothesis tests. Ad-ditionally, the superiority and robustness of our proposed algorithm is testedfor 15 data clustering problems by using both Duncan's multiple range test andthe Nemenyi hypothesis test.
    Źródło:
    Computer Science; 2023, 24 (3); 361--405
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of Proportional-Integral Controllers of Grid-Connected Wind Energy Conversion System Using Grey Wolf Optimizer Based on Artificial Neural Network for Power Quality Improvement
    Autorzy:
    Alremali, Fathi Abdulmajeed M.
    Yaylacı, Ersagun Kürşat
    Uluer, İhsan
    Powiązania:
    https://bibliotekanauki.pl/articles/2201727.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    artificial neural network
    grey wolf optimizer
    PI controller
    grid connection
    power quality
    wind energy
    Opis:
    This research presents a combination of artificial neural network (ANN) with the grey wolf optimizer (GWO) to improve the power quality of a grid-connected distributed power generation system (DPGS). To assess the effectiveness of the proposed algorithm, a grid-tied of small-scale wind energy conversion system (WECS) is chosen. The term power quality refers to voltage and frequency regulation, and limited harmonics. Power quality improvement is achieved through the cascaded control system's optimal tuning of three proportional-integral (PI) controllers of the grid-side inverter (GSI). However, because the DPGS model is computationally costly, the Artificial Neural Network (ANN) model is utilized as an alternative model for DPGS. Furthermore, the ANN model is employed in conjunction with the GWO to boost the optimization precision and minimize the execution time of GWO. The considered power system was repetitively simulated to obtain the input-output datasets, which validate and train the ANN model. According to the ANN model's performance evaluation, the correlation coefficient (R) is close to one, while the mean squared error (MSE) is near zero. These findings demonstrate the ANN model's great accuracy in approximating the DPGS model. Using MATLAB/Simulink, the system's performance is evaluated using the optimum values obtained using GWO-ANN for various wind speed profiles. It showed the suggested power quality method’s improved stability, convergence behavior, the effectiveness of the control mechanism, and the robustness of the proposed topology.
    Źródło:
    Advances in Science and Technology. Research Journal; 2022, 16, 3; 295--305
    2299-8624
    Pojawia się w:
    Advances in Science and Technology. Research Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fractional order PIλDμ controller with optimal parameters using Modified Grey Wolf Optimizer for AVR system
    Autorzy:
    Verma, Santosh Kumar
    Devarapalli, Ramesh
    Powiązania:
    https://bibliotekanauki.pl/articles/2134890.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    integer order PID controller
    fractional order PID controller
    automatic voltage regulator
    evolutionary optimization
    Grey Wolf Optimizer
    Opis:
    In this paper, an automatic voltage regulator (AVR) embedded with fractional order PID (FOPID) is employed for the alternator terminal voltage control. A novel meta-heuristic technique, a modified version of grey wolf optimizer (mGWO) is proposed to design and optimize the FOPID AVR system. The parameters of FOPID, namely, proportional gain (ΚP), the integral gain ( ΚI), the derivative gain ( ΚD), λ and μ have been optimally tuned with the proposed mGWO technique using a novel fitness function. The initial values of the ΚP, ΚI , and ΚD of the FOPID controller are obtained using Ziegler-Nichols (ZN) method, whereas the initial values of λ and μ have been chosen as arbitrary values. The proposed algorithm offers more benefits such as easy implementation, fast convergence characteristics, and excellent computational ability for the optimization of functions with more than three variables. Additionally, the hasty tuning of FOPID controller parameters gives a high-quality result, and the proposed controller also improves the robustness of the system during uncertainties in the parameters. The quality of the simulated result of the proposed controller has been validatedby other state-of-the-art techniques in the literature.
    Źródło:
    Archives of Control Sciences; 2022, 32, 2; 429--450
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Reactive power convex optimization of active distribution network based on Improved GreyWolf Optimizer
    Autorzy:
    Li, Yuancheng
    Yang, Rongyan
    Zhao, Xiaoyu
    Powiązania:
    https://bibliotekanauki.pl/articles/140678.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    active distribution network (ADN)
    Improved Grey Wolf Optimizer (IGWO)
    reactive power optimization
    second-order cone relaxed convex model
    Opis:
    The smart grid concept is predicated upon the pervasive With the construction and development of distribution automation, distributed power supply needs to be comprehensively considered in reactive power optimization as a supplement to reactive power. The traditional reactive power optimization of a distribution network cannot meet the requirements of an active distribution network (ADN), so the Improved Grey Wolf Optimizer (IGWO) is proposed to solve the reactive power optimization problem of the ADN, which can improve the convergence speed of the conventional GWO by changing the level of exploration and development. In addition, a weighted distance strategy is employed in the proposed IGWO to overcome the shortcomings of the conventional GWO. Aiming at the problem that reactive power optimization of an ADN is non-linear and non-convex optimization, a convex model of reactive power optimization of the ADN is proposed, and tested on IEEE33 nodes and IEEE69 nodes, which verifies the effectiveness of the proposed model. Finally, the experimental results verify that the proposed IGWO runs faster and converges more accurately than the GWO.
    Źródło:
    Archives of Electrical Engineering; 2020, 69, 1; 117-131
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of the Grey Wolf Optimizer in the optimization of state space controller for three-mass drive
    Autorzy:
    Żychlewicz, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1189934.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    GWO
    regulator stanu
    układ trójmasowy
    napęd elektryczny
    Grey Wolf Optimizer
    state space controller
    three-mass system
    electrical drive
    Opis:
    This article presents control structure of complex drive that contains two elastic couplings. In the outer control loop the state space controller was implemented. The main point of described work is optimization of parameters used in this part of the drive using metaheuristic algorithm called GWO (Grey Wolf Optimizer). The control structure, designed using mentioned optimization method, was compared to classic solution, known from control theory. High precision of reference speed tracking was achieved. An analysis of the system in the presence of mechanical parameters changes was also prepared. Theoretical considerations were confirmed in numerical tests.
    Źródło:
    Interdisciplinary Journal of Engineering Sciences; 2018, 6, 1; 12--20
    2300-5874
    Pojawia się w:
    Interdisciplinary Journal of Engineering Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Op-Ug TD optimizer tool based on MATLAB code to find transition depth from open pit to block caving
    Narzędzie optymalizacyjne oparte o kod MATLAB wykorzystane do określania głębokości przejściowej od wydobycia odkrywkowego do wybierania komorami
    Autorzy:
    Bakhtavar, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/219228.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    transition
    open pit to block caving
    Op-Ug TD Optimizer
    program
    MATLAB
    przejście
    wydobycie metodami odkrywkowymi
    wydobycie komorami
    program Optymalizator Op-Ug TD
    Opis:
    In this study, transition from open pit to block caving has been considered as a challenging problem. For this purpose, the linear integer programing code of Matlab was initially developed on the basis of the binary integer model proposed by Bakhtavar et al (2012). Then a program based on graphical user interface (GUI) was set up and named “Op-Ug TD Optimizer”. It is a beneficial tool for simple application of the model in all situations where open pit is considered together with block caving method for mining an ore deposit. Finally, Op-Ug TD Optimizer has been explained step by step through solving the transition from open pit to block caving problem of a case ore deposit.
    W pracy tej rozważano skomplikowane zagadnienie przejścia od wybierania odkrywkowego do komorowego. W tym celu opracowano kod programowania liniowego w środowisku MATLAB w oparciu o model liczb binarnych zaproponowany przez Bakhtavara (2012). Następnie opracowano program z wykorzystujący graficzny interfejs użytkownika o nazwie Optymalizator Op-Ug TD. Jest to niezwykle cenne narzędzie umożliwiające stosowanie modelu dla wszystkich warunków w sytuacjach gdy rozważamy prowadzenie wydobycia metodą odkrywkową oraz wydobycie komorowe przy eksploatacji złóż rud żelaza. W końcowej części pracy podano szczegółową instrukcję stosowanie programu Optymalizator na przedstawionym przykładzie przejścia od wydobycia rud żelaza metodami odkrywkowymi poprzez wydobycie komorami.
    Źródło:
    Archives of Mining Sciences; 2015, 60, 3; 687-695
    0860-7001
    Pojawia się w:
    Archives of Mining Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Plug-in direct particle swarm repetitive controller with a reduced dimensionality of a fitness landscape – a multi-swarm approach
    Autorzy:
    Ufnalski, B.
    Grzesiak, L. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/202046.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    repetitive process control
    dynamic optimization problem
    particle swarm optimizer
    repetitive disturbance rejection
    noninteracting subswarms
    dimension-reduced fitness functional
    powtarzalne sterowanie procesem
    problem optymalizacji dynamicznej
    optymalizator rojem cząstek
    odrzucanie zakłóceń
    sprawność funkcjonalna
    Opis:
    The paper describes a modification to the recently developed plug-in direct particle swarm repetitive controller (PDPSRC) for the sine-wave constant-amplitude constant-frequency (CACF) voltage-source inverter (VSI). The original PDPSRC algorithm assumes that the particle swarm optimizer (PSO) takes into account a performance index defined over the whole reference signal period. Each particle stores all the samples of the control signal, e.g. α = 200 samples for a controller working at 10 kHz and the reference frequency equal to 50 Hz. Therefore, the fitness landscape (i.e. the performance index) is -dimensional ( D), which makes optimization challenging. That solution can be categorized as the single-swarm one. It has been previously shown that the swarm controller does not suffer from long-term stability issues encountered in the classic iterative learning controllers (ILC). However, the convergence of the swarm has to be kept at a relatively low rate to enable successful exploitation in the D search space, which in turn results in slow responsiveness of the PDPSRC. Here a multi-swarm approach is proposed in which we divide a dynamic optimization problem (DOP) among less dimensional swarms. The reference signal period is segmented into shorter intervals and the control signal is optimized in each interval independently by separate swarms. The effectiveness of the proposed approach is illustrated with the help of numerical experiments on the CACF VSI with an output LC filter operating under nonlinear loads.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 4; 857-866
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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