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Tytuł:
Feature selection using particle swarm optimization in text categorization
Autorzy:
Aghdam, M. H.
Heidari, S.
Powiązania:
https://bibliotekanauki.pl/articles/91792.pdf
Data publikacji:
2015
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
classification system
feature selection
text categorization
particle swarm optimization (PSO)
system klasyfikacji
wybór funkcji
kategoryzacja tekstu
optymalizacja rojem cząstek
Opis:
Feature selection is the main step in classification systems, a procedure that selects a subset from original features. Feature selection is one of major challenges in text categorization. The high dimensionality of feature space increases the complexity of text categorization process, because it plays a key role in this process. This paper presents a novel feature selection method based on particle swarm optimization to improve the performance of text categorization. Particle swarm optimization inspired by social behavior of fish schooling or bird flocking. The complexity of the proposed method is very low due to application of a simple classifier. The performance of the proposed method is compared with performance of other methods on the Reuters-21578 data set. Experimental results display the superiority of the proposed method.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 4; 231-238
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
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:
ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosis
Autorzy:
Ahsan, Muhammad
Bismor, Dariusz
Manzoor, Muhammad Arslan
Powiązania:
https://bibliotekanauki.pl/articles/27322619.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
signal-to-noise ratio
asymmetric real Laplace wavelet
bandpass filter
particle swarm optimization
spectral kurtosis
fault frequency
Opis:
Rotating element bearings are the backbone of every rotating machine. Vibration signals measured from these bearings are used to diagnose the health of the machine, but when the signal-to-noise ratio is low, it is challenging to diagnose the fault frequency. In this paper, a new method is proposed to enhance the signal-to-noise ratio by applying the Asymmetric Real Laplace wavelet Bandpass Filter (ARL-wavelet-BPF). The Gaussian function of the ARL-wavelet represents an excellent BPF with smooth edges which helps to minimize the ripple effects. The bandwidth and center frequency of the ARL-wavelet-BPF are optimized using the Particle Swarm Optimization (PSO) algorithm. Spectral kurtosis (SK) of the envelope spectrum is employed as a fitness function for the PSO algorithm which helps to track the periodic spikes generated by the fault frequency in the vibration signal. To validate the performance of the ARL-wavelet-BPF, different vibration signals with low signal-to-noise ratio are used and faults are diagnosed.
Źródło:
Archives of Control Sciences; 2023, 33, 3; 589--606
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An arma type pi-sigma artificial neural network for nonlinear time series forecasting
Autorzy:
Akdeniz, E.
Egrioglu, E.
Bas, E.
Yolcu, U.
Powiązania:
https://bibliotekanauki.pl/articles/91816.pdf
Data publikacji:
2018
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
high order artificial neural networks
pi-sigma neural network, forecasting
recurrent neural network
particle swarm optimization (PSO)
Opis:
Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artificial neural network is applied to three real life time series data and also a simulation study is performed for Istanbul Stock Exchange data set.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 2; 121-132
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Cross‐Comparison of Evolutionary Algorithms for Optimizing Design of Sustainable Supply Chain Network under Disruption Risks
Autorzy:
Al-Zuheri, Atiya
Powiązania:
https://bibliotekanauki.pl/articles/2023790.pdf
Data publikacji:
2021
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
comparison
genetic algorithm
particle swarm optimization
sustainable supply chain design
disruption risk
porównanie
algorytm genetyczny
optymalizacja rojem cząstek
projektowanie zrównoważonego łańcucha dostaw
ryzyko zakłóceń
Opis:
Optimization of a sustainable supply chain network design (SSCND) is a complex decision-making process which can be done by the optimal determination of a set of decisions and constraints such as the selection of suppliers, transportation-related facilities and distribution centres. Different optimization techniques have been applied to handle various SSCND problems. Meta- heuristic algorithms are developed from these techniques that are commonly used to solving supply chain related problems. Among them, Genetic algorithms (GA) and particle swarm optimization (PSO) are implemented as optimization solvers to obtain supply network design decisions. This paper aims to compare the performance of these two evolutionary algorithms in optimizing such problems by minimizing the total cost that the system faces to potential disruption risks. The mechanism and implementation of these two evolutionary algorithms is presented in this paper. Also, using an optimization considers ordering, purchasing, inventory, transportation, and carbon tax cost, a numerical real-life case study is presented to demonstrate the validity of the effectiveness of these algorithms. A comparative study for the algorithms performance has been carried out based on the quality of the obtained solution and the results indicate that the GA performs better than PSO in finding lower-cost solution to the addressed SSCND problem. Despite a lot of research literature being done regarding these two algorithms in solving problems of SCND, few studies have compared the optimization performance between GA and PSO, especially the design of sustainable systems under risk disruptions.
Źródło:
Advances in Science and Technology. Research Journal; 2021, 15, 4; 342-351
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming
Autorzy:
Arif, Salah
Djellal, Adel
Djebbari, Nawel
Belhaoues, Saber
Touati, Hassen
Guellati, Fatma Zohra
Bensouilah, Mourad
Powiązania:
https://bibliotekanauki.pl/articles/2174666.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
microcystis cell density
Multiple Linear Regression
Support Vector Machine
Particle Swarm Optimization
Genetic Algorithm
Bird Swarm Algorithm
Opis:
Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.
Źródło:
Geomatics and Environmental Engineering; 2023, 17, 2; 31--68
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Vibroacoustic Real Time Fuel Classification in Diesel Engine
Autorzy:
Bąkowski, A.
Kekez, M.
Radziszewski, L.
Sapietova, A.
Powiązania:
https://bibliotekanauki.pl/articles/177686.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
fuel recognition
classification trees
particle swarm optimization (PSO)
random forest
Opis:
Five models and methodology are discussed in this paper for constructing classifiers capable of recognizing in real time the type of fuel injected into a diesel engine cylinder to accuracy acceptable in practical technical applications. Experimental research was carried out on the dynamic engine test facility. The signal of in-cylinder and in-injection line pressure in an internal combustion engine powered by mineral fuel, biodiesel or blends of these two fuel types was evaluated using the vibro-acoustic method. Computational intelligence methods such as classification trees, particle swarm optimization and random forest were applied.
Źródło:
Archives of Acoustics; 2018, 43, 3; 385-395
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method
Autorzy:
Balci, H. H.
Valenzuela, J. F.
Powiązania:
https://bibliotekanauki.pl/articles/907641.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
optymalizacja rojem cząstek
jednostka wytwórcza
relaksacja Lagrange'a
particle swarm optimization (PSO)
unit commitment
Lagrange relaxation
Opis:
This paper describes a procedure that uses particle swarm optimization (PSO) combined with the Lagrangian Relaxation (LR) framework to solve a power-generator scheduling problem known as the unit commitment problem (UCP). The UCP consists of determining the schedule and production amount of generating units within a power system subject to operating constraints. The LR framework is applied to relax coupling constraints of the optimization problem. Thus, the UCP is separated into independent optimization functions for each generating unit. Each of these sub-problems is solved using Dynamic Programming (DP). PSO is used to evolve the Lagrangian multipliers. PSO is a population based search technique, which belongs to the swarm intelligence paradigm that is motivated by the simulation of social behavior to manipulate individuals towards better solution areas. The performance of the PSO-LR procedure is compared with results of other algorithms in the literature used to solve the UCP. The comparison shows that the PSO-LR approach is efficient in terms of computational time while providing good solutions.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 411-421
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid MPPT algorithm for PV systems under partially shaded conditions using a stochastic evolutionary search and a deterministic hill climbing
Autorzy:
Basiński, K.
Ufnalski, B.
Grzesiak, L. M.
Powiązania:
https://bibliotekanauki.pl/articles/1193446.pdf
Data publikacji:
2017
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
maximum power point tracking
photovoltaic system
hybrid part-stochastic part-deterministic search rule
particle swarm optimization (PSO)
partial shading
hill climbing
Opis:
A hybrid maximum power point tracking method has been proposed for the photovoltaic system using a stochastic evolutionary search and a deterministic hill climbing algorithm. The proposed approach employs the particle swarm optimizer (PSO) to solve a dynamic optimization problem related to the control task in a PV system. The position of the best particle is updated by the hill climbing algorithm, and the position of the rest of the particles by the classic PSO rule. The presented method uses the re-randomization mechanism, which places five consecutive particles randomly, but in specified intervals. This mechanism helps track the maximum power point under partially shaded conditions.
Źródło:
Power Electronics and Drives; 2017, 2, 37/2; 49-59
2451-0262
2543-4292
Pojawia się w:
Power Electronics and Drives
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predicting and minimizing the blasting cost in limestone mines using a combination of gene expression programming and particle swarm optimization
Autorzy:
Bastami, Reza
Bazzazi, Abbas Aghajani
Shoormasti, Hadi Hamidian
Ahangari, Kaveh
Powiązania:
https://bibliotekanauki.pl/articles/1853861.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
kopalnia wapienia
wybuch detonacyjny
regresja nieliniowa
blasting cost
limestone mine
gene expression programming
non-linear multivariate regression
particle swarm optimization algorithm
environmental impacts
Opis:
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.
Źródło:
Archives of Mining Sciences; 2020, 65, 4; 835-850
0860-7001
Pojawia się w:
Archives of Mining Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Particle swarm optimization of a neural network model for predicting the flashover voltage on polluted cap and pin insulator
Autorzy:
Belkebir, Amel
Bourek, Yacine
Benguesmia, Hani
Powiązania:
https://bibliotekanauki.pl/articles/2146737.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
flashover voltage
particle swarm optimization
prediction
artificial pollution
neural network
napięcie przeskoku
optymalizacja roju cząstek
prognozowanie
sieć neuronowa
Opis:
This paper proposes training an artificial neural network (ANN) by a particle swarm optimization (PSO) technique to predict the flashover voltage of outdoor insulators. The analysis follows a series of real-world tests on high-voltage insulators to form a database for implementing artificial intelligence concepts. These tests are performed in various degrees of artificial contamination (distilled brine). Each contamination level shows the amount of contamination in milliliters per area of the isolator. The acquisition database provides values of flashover voltage corresponding to their electrical conductivity in each isolation zone and different degrees of artificial contamination. The results show that ANN trained by PSO can not only provide better prediction results, but also reduce the amount of computation efforts. It is also a more powerful model because: it does not get stuck in a local optimum. In addition, it also has the advantages of simple logic, simple implementation, and underlying intelligence. Compared to the results obtained by practical tests, the results obtained present that the PSO-ANN technique is very effective to predict flashover of high-voltage polluted insulators.
Źródło:
Diagnostyka; 2022, 23, 3; art. no 2022309
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid method for the optimal reactive power dispatch and the control of voltagesin an electrical energy network
Autorzy:
Benchabira, Aissa
Khiat, Mounir
Powiązania:
https://bibliotekanauki.pl/articles/140740.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
electrical energy network
interior point method (IPM)
optimal reactive power dispatch (ORPD)
particle swarm optimization (PSO)
Opis:
This paper presents the resolution of the optimal reactive power dispatch (ORPD) problem and the control of voltages in an electrical energy system by using a hybrid algorithm based on the particle swarmoptimization (PSO) method and interior point method (IPM). The IPM is based on the logarithmic barrier (LB-IPM) technique while respecting the non-linear equality and inequality constraints. The particle swarmoptimization-logarithmic barrier-interior point method (PSO-LB-IPM) is used to adjust the control variables, namely the reactive powers, the generator voltages and the load controllers of the transformers, in order to ensure convergence towards a better solution with the probability of reaching the global optimum. The proposed method was first tested and validated on a two-variable mathematical function using MATLAB as a calculation and execution tool, and then it is applied to the ORPD problem to minimize the total active losses in an electrical energy network. To validate the method a testwas carried out on the IEEE electrical energy network of 57 buses.
Źródło:
Archives of Electrical Engineering; 2019, 68, 3; 535-551
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Control imrovement of shunt active power filter using an optimized-PI controller based on ant colony algorithm and swarm optimization
Autorzy:
Berbaoui, B.
Ferdi, B.
Benachaiba, C.
Dehini, R.
Powiązania:
https://bibliotekanauki.pl/articles/385137.pdf
Data publikacji:
2010
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
ant colony optimization
particle swarm optimization (PSO)
shunt active power filter
armonic compensation
PI controller
Opis:
In the last years, there has been a increase currents harmonics on electrical network injected by nonlinear loads, such as rectifier equipment used in telecommunication system, power suppliers, domestic appliances, ect. This paper makes a comparison of the effectiveness of the two methods on particular optimization problem, namely. The tuning of the parameters for PI DC link voltage to a shunt active power filter. The simulation results demonstrates that the optimized PI controller by ant colony (ACO) presents a advantage of little response time and best control performances compared to the optimized PI with Particle swarm (PSO). This comparison is shown on redu cing harmonic current supply (THD).
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2010, 4, 4; 19-25
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dynamics of Stochastic vs. Greedy Heuristics in Traveling Salesman Problem
Autorzy:
Białogłowski, M.
Staniaszek, M.
Laskowski, W.
Grudniak, M.
Powiązania:
https://bibliotekanauki.pl/articles/91276.pdf
Data publikacji:
2018
Wydawca:
Warszawska Wyższa Szkoła Informatyki
Tematy:
traveling salesman problem
Nearest Neighbor
Monte Carlo
Simulated Annealing
Genetic Algorithm
particle swarm optimization (PSO)
Opis:
We studied the relative performance of stochastic heuristics in order to establish the relations between the fundamental elements of their mechanisms. The insights on their dynamics, abstracted from the implementation details, may contribute to the development of an efficient framework for design of new probabilistic methods. For that, we applied four general optimization heuristics with varying number of hyperparameters to traveling salesman problem. A problem-specific greedy approach (Nearest Neighbor) served as a reference for the results of: Monte Carlo, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization. The more robust heuristics – with higher configuration potential, i.e. with more hyperparameters – outperformed the smart ones, being surpassed only by the method specifically designed for the task.
Źródło:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2018, 12, 19; 7-24
1896-396X
2082-8349
Pojawia się w:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effectiveness of the MPSO algorithm in optimization of the coil arrangement
Skuteczność algorytmu MPSO w optymalizacji układu cewek
Autorzy:
Borowska, B.
Powiązania:
https://bibliotekanauki.pl/articles/159534.pdf
Data publikacji:
2010
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Elektrotechniki
Tematy:
elektromagnetyzm
optymalizacja
algorytm PSO
pole magnetyczne
electromagnetism
optimization
particle swarm optimization (PSO)
magnetic field
Opis:
One of the most important problems in designing of various constructions is optimization of technical facilities. The optimization process leads to find the best solution of a considered problem, and the solution should meet established criteria. Evolutionary algorithms have been found to be effective in solving such optimization problems. In the following paper, a modification of the PSO algorithm has been proposed in order to determine an optimal geometry of the coil arrangement evoking, in a defined active area, magnetic field of the largest possible gradient, and simultaneously keep this gradient relatively stable. The computations confirmed high efficiency of the proposed method. The results were also compared with the achievements of other evolutionary algorithms.
Jednym z najważniejszych zagadnień w projektowaniu różnych konstrukcji jest optymalizacja urządzeń technicznych. Jej celem jest znalezienie najlepszego rozwiązania rozpatrywanego zagadnienia o najlepszych w sensie przyjętych kryteriów parametrach. Do rozwiązywania tego typu zadań m.in. stosuje się algorytmy ewolucyjne. Aby algorytm był skuteczny często niezbędne jest jednak przeprowadzenie bardzo dużej liczby obliczeń. W celu redukcji kosztów obliczeń w artykule zaproponowano algorytm MPSO będący modyfikacją algorytmu PSO do problemu wyznaczenia optymalnej konstrukcji. Zadaniem zaproponowanego algorytmu było wyznaczenie optymalnej geometrii układu cewek generujących w zdefiniowanym obszarze aktywnym pola magnetycznego o możliwie dużym gradiencie przy zachowaniu jak największej stałości tego gradientu. Na podstawie przeprowadzonych badań, dokonano porównania efektywności zaproponowanej metody MPSO z osiągnięciami standardowego algorytmu optymalizacji cząsteczkowej PSO oraz algorytmu Θ-PSO zaproponowanego przez Zhong i innych [24]. Przeprowadzone obliczenia potwierdziły skuteczność algorytmu MPSO.
Źródło:
Prace Instytutu Elektrotechniki; 2010, 246; 35-44
0032-6216
Pojawia się w:
Prace Instytutu Elektrotechniki
Dostawca treści:
Biblioteka Nauki
Artykuł
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