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Wyświetlanie 1-15 z 146
Tytuł:
Magnetic Particle Swarm Optimization
Autorzy:
Prampero, P. S.
Attux, R.
Powiązania:
https://bibliotekanauki.pl/articles/91715.pdf
Data publikacji:
2012
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
Magnetic Particle Swarm Optimization
multimodal search
metaheuristics
sensitivity analysis
convergence
Opis:
This paper presents and analyzes a search paradigm called Magnetic Particle Swarm Optimization. This paradigm gives support to two algorithms that combine elements of the behavior of magnetic dipoles within a framework that includes several elements that are known to be essential to effective multimodal search. The algorithms are applied to a variety of functions and their performance is compared with those of a number of related well-established metaheuristics. In addition to that, convergence and sensitivity analyses are presented for the first time.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 1; 59-72
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Celestial navigation fix based on particle swarm optimization
Autorzy:
Tsou, M.-C.
Powiązania:
https://bibliotekanauki.pl/articles/258524.pdf
Data publikacji:
2015
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
particle swarm optimization (PSO)
Celestial navigation
Intercept method
Opis:
A technique for solving celestial fix problems is proposed in this study. This method is based on Particle Swarm Optimization from the field of swarm intelligence, utilizing its superior optimization and searching abilities to obtain the most probable astronomical vessel position. In addition to being applicable to two-body fix, multi-body fix, and high-altitude observation problems, it is also less reliant on the initial dead reckoning position. Moreover, by introducing spatial data processing and display functions in a Geographical Information System, calculation results and chart work used in Circle of Position graphical positioning can both be integrated. As a result, in addition to avoiding tedious and complicated computational and graphical procedures, this work has more flexibility and is more robust when compared to other analytical approaches.
Źródło:
Polish Maritime Research; 2015, 3; 20-27
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Repulsive self - adaptive acceleration particle swarm optimization approach
Autorzy:
Ludwig, S. A.
Powiązania:
https://bibliotekanauki.pl/articles/91874.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
adaptive Particle Swarm Optimization
adaptive PSO
optimization
Repulsive Self-adaptive Acceleration PSO
RSAPSO
velocity weights
optimal solution of the problem
function evaluations
Opis:
Adaptive Particle Swarm Optimization (PSO) variants have become popular in recent years. The main idea of these adaptive PSO variants is that they adaptively change their search behavior during the optimization process based on information gathered during the run. Adaptive PSO variants have shown to be able to solve a wide range of difficult optimization problems efficiently and effectively. In this paper we propose a Repulsive Self-adaptive Acceleration PSO (RSAPSO) variant that adaptively optimizes the velocity weights of every particle at every iteration. The velocity weights include the acceleration constants as well as the inertia weight that are responsible for the balance between exploration and exploitation. Our proposed RSAPSO variant optimizes the velocity weights that are then used to search for the optimal solution of the problem (e.g., benchmark function). We compare RSAPSO to four known adaptive PSO variants (decreasing weight PSO, time-varying acceleration coefficients PSO, guaranteed convergence PSO, and attractive and repulsive PSO) on twenty benchmark problems. The results show that RSAPSO achives better results compared to the known PSO variants on difficult optimization problems that require large numbers of function evaluations.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 3; 189-204
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
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:
Particle swarm optimization algorithm based low cost magnetometer calibration
Autorzy:
Ali, A.
Siddharth, S.
Syed, Z.
El-Sheimy, N.
Powiązania:
https://bibliotekanauki.pl/articles/129567.pdf
Data publikacji:
2011
Wydawca:
Stowarzyszenie Geodetów Polskich
Tematy:
artificial intelligence
systems
measurement
navigation
algorithm
sensor
sztuczna inteligencja
systemy
pomiar
nawigacja
algorytm
Opis:
Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a microprocessor provide inertial digital data from which position and orientation is obtained by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the absolute user heading based on Earth’s magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are corrupted by several errors including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO) based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometer. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. The estimated bias and scale factor errors from the proposed algorithm improve the heading accuracy and the results are also statistically significant. Also, it can help in the development of the Pedestrian Navigation Devices (PNDs) when combined with the INS and GPS/Wi-Fi especially in the indoor environments.
Źródło:
Archiwum Fotogrametrii, Kartografii i Teledetekcji; 2011, 22; 9-23
2083-2214
2391-9477
Pojawia się w:
Archiwum Fotogrametrii, Kartografii i Teledetekcji
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Design of a Predictive PID Controller Using Particle Swarm Optimization
Autorzy:
Mustafa, Norhaida
Hashim, Fazida Hanim
Powiązania:
https://bibliotekanauki.pl/articles/1844451.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
proportional integral derivative controller
particle swarm optimization (PSO) algorithm
optimization
predictive PID
Opis:
The proportional-integral-derivative (PID) controller is widely used in various industrial applications such as process control, motor drives, magnetic and optical memory, automotive, flight control and instrumentation. PID tuning refers to the generation of PID parameters (Kp, Ki, Kd) to obtain the optimum fitness value for any system. The determination of the PID parameters is essential for any system that relies on it to function in a stable mode. This paper proposes a method in designing a predictive PID controller system using particle swarm optimization (PSO) algorithm for direct current (DC) motor application. Extensive numerical simulations have been done using the Mathwork’s Matlab simulation environment. In order to gain full benefits from the PSO algorithm, the PSO parameters such as inertia weight, iteration number, acceleration constant and particle number need to be carefully adjusted and determined. Therefore, the first investigation of this study is to present a comparative analysis between two important PSO parameters; inertia weight and number of iteration, to assist the predictive PID controller design. Simulation results show that inertia weight of 0.9 and iteration number 100 provide a good fitness achievement with low overshoot and fast rise and settling time. Next, a comparison between the performance of the DC motor with PID-PSO, with PID of gain 1, and without PID were also discussed. From the analysis, it can be concluded that by tuning the PID parameters using PSO method, the best gain in performance may be found. Finally, when comparing between the PID-PSO and its counterpart, the PI-PSO, the PID-PSO controller gives better performance in terms of robustness, low overshoot (0.005%), low minimum rise time (0.2806 seconds) and low settling time (0.4326 seconds).
Źródło:
International Journal of Electronics and Telecommunications; 2020, 66, 4; 737-743
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Particle swarm optimization for tuning PSS-PID controller of synchronous generator
Autorzy:
Derrar, A.
Naceri, A.
Powiązania:
https://bibliotekanauki.pl/articles/384775.pdf
Data publikacji:
2017
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
synchronous generator
PSS
particle swarm optimization (PSO)
PID controller
Opis:
In this paper the design an optimal PSS-PID controller for single machine connected to an infinite bus (SMIB). We presented a novel application of particle swarm optimization (PSO) for the optimal tuning of the new PSS-PID controller. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. The synchronous generator is modeled and the PSO algorithm is implemented in Simulink of Matlab. The obtained results have proved that (PSO) are a powerful tools for optimizing the PSS parameters, and more robustness of the system IEEE SMIB.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2017, 11, 1; 48-52
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Estimation of composite load model parameters using improved particle swarm optimization
Autorzy:
Regulski, P.
Gonzalez-Longatt, F.
Terzija, V.
Powiązania:
https://bibliotekanauki.pl/articles/410557.pdf
Data publikacji:
2012
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
load modeling
parameter estimation
particle swarm optimization (PSO)
composite load model
Opis:
Power system loads are one of its crucial elements to be modeled in stability studies. However their static and dynamic characteristics are very often unknown and usually changing in time (daily, weekly, monthly and seasonal variations). Taking this into account, a measurement-based approach for determining the load characteristics seems to be the best practice, as it updates the parameters of a load model directly from the system measurements. To achieve this, a Parameter Estimation tool is required, so a common approach is to incorporate the standard Nonlinear Least Squares, or Genetic Algorithms, as a method providing more global capabilities. In this paper a new solution is proposed -an Improved Particle Swarm Optimization method. This method is an Artificial Intelligence type technique similar to Genetic Algorithms, but easier for implementation and also computationally more efficient. The paper provides results of several experiments proving that the proposed method can achieve higher accuracy and show better generalization capabilities than the Nonlinear Least Squares method. The computer simulations were carried out using a one-bus and an IEEE 39-bus test system.
Źródło:
Present Problems of Power System Control; 2012, 2; 41-51
2084-2201
Pojawia się w:
Present Problems of Power System Control
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The influence of inertia weight on the Particle Swarm Optimization algorithm
Autorzy:
Cekus, D.
Skrobek, D.
Powiązania:
https://bibliotekanauki.pl/articles/122644.pdf
Data publikacji:
2018
Wydawca:
Politechnika Częstochowska. Wydawnictwo Politechniki Częstochowskiej
Tematy:
particle swarm optimization (PSO)
PSO algorithm
inertia weight
trajectory
optymalizacja rojem cząstek
PSO
algorytm PSO
metoda PSO
algorytm optymalizacji rojem cząstek
trajektoria
współczynnik wagowy
Opis:
The paper presents the use of the Particle Swarm Optimization (PSO) algorithm to find the shortest trajectory connecting two defined points while avoiding obstacles. The influence of the inertia weight and the number of population adopted in the first iteration of the PSO algorithm was examined for the length of the sought trajectory. Simulation results showed that the proposed method achieved significant improvement compared to the linearly decreasing method technique that is widely used in literature.
Źródło:
Journal of Applied Mathematics and Computational Mechanics; 2018, 17, 4; 5-11
2299-9965
Pojawia się w:
Journal of Applied Mathematics and Computational Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Computationally efficient nonlinear model predictive controller using parallel particle swarm optimization
Autorzy:
Diwan, Supriya P.
Deshpande, Shraddha S.
Powiązania:
https://bibliotekanauki.pl/articles/2173694.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
nonlinear model predictive control
particle swarm optimization
PSO
fast dynamic systems
rotary inverted pendulum
divide approach
conquer approach
kontrola predykcyjna modelu nieliniowa
optymalizacja roju cząstek
system dynamiczny szybki
wahadło obrotowe odwrócone
Opis:
As nonlinear optimization techniques are computationally expensive, their usage in the real-time era is constrained. So this is the main challenge for researchers to develop a fast algorithm that is used in real-time computations. This work proposes a fast nonlinear model predictive control approach based on particle swarm optimization for nonlinear optimization with constraints. The suggested algorithm divide and conquer technique improves computing speed and disturbance rejection capability, demonstrating its suitability for real-time applications. The performance of this approach under constraints is validated using a highly nonlinear fast and dynamic real-time inverted pendulum system. The solution presented through work is computationally feasible for smaller sampling times and it gives promising results compared to the state of art PSO algorithm
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 4; art. no. e140696
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Advanced Particle Swarm Optimization Algorithm for MPPTs in PV Systems
Autorzy:
Erdem, Z.
Powiązania:
https://bibliotekanauki.pl/articles/1031910.pdf
Data publikacji:
2017-09
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
87.55.de
88.40.mp
87.55.kd
Opis:
Maximum power point trackers are in charge of absorbing the maximum potential power from the photovoltaic panels. Thus, this makes the maximum power point trackers the fundamental parts of the photovoltaic panel systems. The conventional maximum power point tracker algorithms are working well under balanced insolation conditions, however when the partial shade condition occurs, those algorithms are trapped at the local maxima. Hence, under partial shade conditions, the need for a global maximum power point tracking algorithm arises. Particle swarm optimization is a preferential algorithm of maximum power point trackers in literature, especially in partial shade conditions. This paper is focused on improving the existing particle swarm optimization algorithm for maximum power point trackers. The proposed advanced particle swarm optimization algorithm aims to catch the global maximum power point much faster, accurately and to reduce the chatter in the power curve. The proposed method accelerates the global maximum tracking time with gridding the initial search area. The effectiveness of the proposed method is demonstrated with simulation results and these results are compared with a conventional particle swarm optimization method under step changes in irradiance and partial shade conditions of an array of photovoltaic panels.
Źródło:
Acta Physica Polonica A; 2017, 132, 3; 1134-1139
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Particle Swarm Optimization Algorithm for Leakage Power Reduction in VLSI Circuits
Autorzy:
Leela Rani, V.
Madhavi Latha, M.
Powiązania:
https://bibliotekanauki.pl/articles/225990.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
leakage power
PSO algorithm
genetic algorithm
minimum leakage vector
Verilog-HDL implementation
Opis:
Leakage power is the dominant source of power dissipation in nanometer technology. As per the International Technology Roadmap for Semiconductors (ITRS) static power dominates dynamic power with the advancement in technology. One of the well-known techniques used for leakage reduction is Input Vector Control (IVC). Due to stacking effect in IVC, it gives less leakage for the Minimum Leakage Vector (MLV) applied at inputs of test circuit. This paper introduces Particle Swarm Optimization (PSO) algorithm to the field of VLSI to find minimum leakage vector. Another optimization algorithm called Genetic algorithm (GA) is also implemented to search MLV and compared with PSO in terms of number of iterations. The proposed approach is validated by simulating few test circuits. Both GA and PSO algorithms are implemented in Verilog HDL and the simulations are carried out using Xilinx 9.2i. From the simulation results it is found that PSO based approach is best in finding MLV compared to Genetic based implementation as PSO technique uses less runtime compared to GA. To the best of the author’s knowledge PSO algorithm is used in IVC technique to optimize power for the first time and it is quite successful in searching MLV.
Źródło:
International Journal of Electronics and Telecommunications; 2016, 62, 2; 179-186
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm
Autorzy:
Dziwiński, Piotr
Bartczuk, Łukasz
Paszkowski, Józef
Powiązania:
https://bibliotekanauki.pl/articles/1837533.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
hybrid methods
Particle Swarm Optimization
Genetic Algorithm
fuzzy systems
multimodal function
Opis:
The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 95-111
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Particle Swarm Optimization Fuzzy Systems for the Age Reduction Imperfect Maintenance Model
Autorzy:
Li, Che-Hua
Powiązania:
https://bibliotekanauki.pl/articles/301843.pdf
Data publikacji:
2008
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
imperfect maintenance
preventive maintenance
reliability
fuzzy modeling
particle swarm optimization (PSO)
Opis:
This research includes two topics: (1) the modeling of periodic preventive maintenance policies over an infi nite time span for repairable systems with the reduction of the degradation rate after performing an imperfect preventive maintenance (PM) activity; (2) the parameter estimation of failure distribution and the restoration effect of PM from the proposed PM policy for deteriorating systems. The concept of the improvement factor method is applied to measure the restoration effect on the degradation rate for a system after each PM. An improvement factor is presented as a function of the system's age and the cost of each PM. A periodic PM model is then developed. The optimal PM interval and the optimal replacement time for the proposed model can be obtained by minimizing the objective functions of the cost rate through the algorithms provided by this research. An example of using Weibull failure distribution is provided to investigate the proposed model. The method is proposed to estimate the parameters of the failure process and the improvement effect after each PM by analyzing maintenance and failure log data. In this method, a PSO-based method is proposed for automatically constructing a fuzzy system with an appropriate number of rules to approach the identifi ed system. In the PSO-based method, each individual in the population is constructed to determine the number of fuzzy rules and the premise part of the fuzzy system, and then the recursive least-squares method is used to determine the consequent part of the fuzzy system constructed by the corresponding individual. Consequently, an individual corresponds to a fuzzy system. Subsequently, a fi tness function is defi ned to guide the searching procedure to select an appropriate fuzzy system with the desired performance. Finally, two identifi cation problems of nonlinear systems are utilized to illustrate the effectiveness of the proposed method for fuzzy modeling.
Źródło:
Eksploatacja i Niezawodność; 2008, 4; 28-34
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Unsupervised classification and particle swarm optimization
Klasyfikacja nienadzorowana i optymalizacja rojem cząstek
Autorzy:
Truszkowski, A.
Topczewska, M.
Powiązania:
https://bibliotekanauki.pl/articles/341179.pdf
Data publikacji:
2012
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
klasyfikacja nienadzorowana
analiza skupień
optymalizacja rojem cząstek
unsupervised classification
clustering
particle swarm optimization (PSO)
Opis:
This article considers three algorithms of unsupervised classification -K-means, Gbest and the Hybrid method, the last two have been proposed in [14]. All three algorithms belong to the class of non-hierarchical methods. At first, the initial split of objects into known in advance number of classes is performed. If it is necessary, some objects are then moved into other clusters to achieve better split - between cluster variation should be much larger than within cluster variation. The first algorithm described in this paper (K-means) is wellknown classical method. The second one (Gbest) is based on the particle swarm intelligence idea. While the third is a hybrid of two mentioned algorithms. Several indices assessing the quality of obtained clusters are calculated.
W niniejszym artykule porównywane są trzy algorytmy analizy skupień - metoda k-średnich, algorytm gbest oraz metoda hybrydowa. Algorytmy gbest oraz hybrydowy zostały zaproponowane w publikacji [14]. Wszystkie trzy metody nalezą a do rodziny metod niehierarchicznych, w których na początku tworzony jest podział obiektów na znaną z góry liczbę klastrów. Następnie, niektóre obiekty przenoszone są pomiędzy klastrami, by uzyskać jak najlepszy podział - wariancja pomiędzy skupieniami powinna być znacznie większa niż wariancja wewnątrz skupień. Pierwszy algorytm (k-means) jest znaną, klasyczną metodą. Drugi oparty jest na idei inteligencji roju cząstek. Natomiast trzeci jest metodą hybrydową łączącą dwa wymienione wcześniej algorytmy. Do porównania uzyskanych skupień wykorzystano kilka różnych indeksów szacujących jakość otrzymanych skupień.
Źródło:
Zeszyty Naukowe Politechniki Białostockiej. Informatyka; 2012, 9; 119-132
1644-0331
Pojawia się w:
Zeszyty Naukowe Politechniki Białostockiej. Informatyka
Dostawca treści:
Biblioteka Nauki
Artykuł
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