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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:
Collision-free autonomous robot navigation in unknown environments utilizing PSO for path planning
Autorzy:
Krell, Evan
Sheta, Alaa
Balasubramanian, Arun Prassanth Ramaswamy
King, Scott A.
Powiązania:
https://bibliotekanauki.pl/articles/91555.pdf
Data publikacji:
2019
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
mobile robot
particle swarm optimization (PSO)
path planning
Opis:
The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 4; 267-282
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative Study of Particle Swarm Optimization and Genetic Algorithms for Complex Mathematical Functions
Autorzy:
Valdez, F.
Melin, P.
Powiązania:
https://bibliotekanauki.pl/articles/384575.pdf
Data publikacji:
2008
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
genetic algorithms
particle swarm optimization (PSO)
hybrid systems
optimization
Opis:
The Particle Swarm Optimization (PSO) and the Genetic Algorithms (GA) have been used successfully in solving problems of optimization with continuous and combinatorial search spaces. In this paper the results of the application of PSO and GAs for the optimization of mathematical functions are presented. These two methodologies have been implemented with the goal of making a comparison of their performance in solving complex optimization problems. This paper describes a comparison between a GA and PSO for the optimization of complex mathematical functions.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 1; 43-51
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
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:
Propeller optimization for small unmanned aerial vehicles
Autorzy:
Kusznir, T.
Smoczek, J.
Powiązania:
https://bibliotekanauki.pl/articles/246608.pdf
Data publikacji:
2017
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
unmanned aerial vehicles
particle swarm optimization (PSO)
airfoil modelling
Opis:
Small-unmanned aerial vehicle propellers usually have a low figure of merit due to operating in the low Reynold’s number region due to their size and velocity. The airflow on the airfoil becomes increasingly laminar in this region thus increasing the profile drag and consequently reducing the figure of merit of the rotor. In the article, the airfoil geometries are parameterized using the Class/Shape function transformation. Particle swarm optimization is used to design an airfoil, operating in a Reynolds number of 100,000, which has a high lift to drag ratio. To avoid exceeding geometric constraints of the airfoil, a deterministic box constraint is added to the algorithm. The optimized airfoil is then used for a preliminary design of a rotor; given some design, constraints on the tip chord the rotor radius and the blade root chord, with parameters that achieve the highest theoretical figure of merit. The rotor parameters are obtained using a combination of momentum theory and blade element theory. The figure of merit of an optimal propeller with the same geometric parameters is then compared using the optimized airfoil and the Clark Y airfoil. The optimization is done in MATLAB while the aerodynamic coefficients are obtained from XFOIL. The results of the numerical simulation are presented in the article.
Źródło:
Journal of KONES; 2017, 24, 2; 125-132
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The multi-constrained multicast routing improved by hybrid bacteria foraging-particle swarm optimization
Autorzy:
Sahoo, Satya Prakash
Kabat, Manas Ranjan
Powiązania:
https://bibliotekanauki.pl/articles/305674.pdf
Data publikacji:
2019
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
QoS routing
multicasting
bacteria foraging optimization
particle swarm optimization (PSO)
Opis:
To solve multicast routing under multiple constraints, it is required to generate a multicast tree that ranges from a source to the destinations with minimum cost subject to several constraints. In this paper, PSO has been embedded with BFO to improve the convergence speed and avoid premature convergence that will be used for solving QoS multicast routing problem. The algorithm proposed here generates a set of delay compelled links to every destination present in the multicast group. Then the Bacteria Foraging Algorithm (BFA) selects the paths to all the destinations sensibly from the set of least delay paths to construct a multicast tree. The robustness of the algorithm being proposed had been established through the simulation. The efficiency and effectiveness of the algorithm being proposed was validated through the comparison study with other existing meta-heuristic algorithms. It shows that our proposed algorithm IBF-PSO outperforms its competitive algorithms.
Źródło:
Computer Science; 2019, 20 (2); 245-269
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Toward the best combination of optimization with fuzzy systems to obtain the best solution for the GA and PSO algorithms using parallel processing
Autorzy:
Valdez, Fevrier
Kawano, Yunkio
Melin, Patricia
Powiązania:
https://bibliotekanauki.pl/articles/384329.pdf
Data publikacji:
2020
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
genetic algorithms
particle swarm optimization (PSO)
fuzzy logic
parallel processing
Opis:
In general, this paper focuses on finding the best configuration for PSO and GA, using the different migration blocks, as well as the different sets of the fuzzy systems rules. To achieve this goal, two optimization algorithms were configured in parallel to be able to integrate a migration block that allow us to generate diversity within the subpopulations used in each algorithm, which are: the particle swarm optimization (PSO) and the genetic algorithm (GA). Dynamic parameter adjustment was also performed with a fuzzy system for the parameters within the PSO algorithm, which are the following: cognitive, social and inertial weight parameter. In the GA case, only the crossover parameter was modified.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 1; 55-64
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On the efficiency of population-based optimization in finding best parameters for RGB-D visual odometry
Autorzy:
Kostusiak, Aleksander
Skrzypczyński, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/384397.pdf
Data publikacji:
2019
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
particle swarm optimization (PSO)
evolutionary algorithm
visual odometry
RGB-D
Opis:
Visual odometry estimates the transformations between consecutive frames of a video stream in order to recover the camera’s trajectory. As this approach does not require to build a map of the observed environment, it is fast and simple to implement. In the last decade RGBD cameras proliferated in roboTIcs, being also the sensors of choice for many practical visual odometry systems. Although RGB-D cameras provide readily available depth images, that greatly simplify the frame-to-frame transformations computaTIon, the number of numerical parameters that have to be set properly in a visual odometry system to obtain an accurate trajectory estimate remains high. Whereas seƫng them by hand is certainly possible, it is a tedious try-and-error task. Therefore, in this article we make an assessment of two population-based approaches to parameter opTImizaTIon, that are for long time applied in various areas of robotics, as means to find best parameters of a simple RGB-D visual odometry system. The optimization algorithms investigated here are particle swarm optimization and an evolutionary algorithm variant. We focus on the optimization methods themselves, rather than on the visual odometry algorithm, seeking an efficient procedure to find parameters that minimize the estimated trajectory errors. From the experimental results we draw conclusions as to both the efficiency of the optimization methods, and the role of particular parameters in the visual odometry system.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2019, 13, 2; 5-14
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
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:
Particle swarm optimization of an iterative learning controller for the single-phase inverter with sinusoidal output voltage waveform
Autorzy:
Ufnalski, B.
Grzesiak, L. M.
Gałkowski, K.
Powiązania:
https://bibliotekanauki.pl/articles/200271.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
iterative learning control
sine wave inverter
particle swarm optimization (PSO)
Opis:
This paper presents the application of a particle swarm optimization (PSO) to determine iterative learning control (ILC) law gains for an inverter with an LC output filter. Available analytical tuning methods derived for a given type of ILC law are not very straightforward if additional performance requirements of the closed-loop system have to be met. These requirements usually concern the dynamics of a response to a reference signal, the dynamics of a disturbance rejection, the immunity against expected level of system and measurement noise, the robustness to anticipated variations of parameters, etc. An evolutionary optimization approach based on the swarm intelligence is proposed here. It is shown that in the case of the ILC applied to the LC filter, a cost function based on mean squares can produce satisfactory tuning effects. The efficacy of the procedure is illustrated by performing the optimization for various noise levels and various requested dynamics.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 3; 649-660
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Function optimization using metaheuristics
Autorzy:
Pilski, M.
Seredyński, F.
Powiązania:
https://bibliotekanauki.pl/articles/92887.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
particle swarm optimization (PSO)
artificial immune system
genetic algorithm
function optimization
Opis:
The paper presents the results of comparison of three metaheuristics that currently exist in the problem of function optimization. The first algorithm is Particle Swarm Optimization (PSO) - the algorithm has recently emerged. The next one is based on a paradigm of Artificial Immune System (AIS). Both algorithms are compared with Genetic Algorithm (GA). The algorithms are applied to optimize a set of functions well known in the area of evolutionary computation. Experimental results show that it is difficult to unambiguously select one best algorithm which outperforms other tested metaheuristics.
Źródło:
Studia Informatica : systems and information technology; 2006, 1(7); 77-91
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Increased Performance of a Hybrid Optimizer for Simulation Based Controller Parameterization
Autorzy:
Neugebauer, R.
Hipp, K.
Hellmich, A.
Schlegel, H.
Powiązania:
https://bibliotekanauki.pl/articles/384707.pdf
Data publikacji:
2012
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
controller parameterization
simulation based optimization
particle swarm optimization (PSO)
Nelder-Mead
Opis:
The controller parameterization is often carried out by applying basic empirical formulas within an integrated automatic design. Hence, the determined settings are often insufficiently verified by the resulting system behavior. In this paper an approach for the controller parameterization by using methods of simulation based optimization is presented. This enables the user to define specific restrictions e.g. the complementary sensitivity function (CSF) to influence the dynamic behavior of the control loop. Furthermore it is possible to choose alternative optimization criteria. A main influence factor for practical offline as well as controller internal optimization methods is the execution time, which can be reduced by applying a hybrid optimization strategy. Thus, the paper presents a performance comparison between the straight global Particle-Swarm-Optimization (PSO) algorithm and the combination of the global PSO with the local optimization algorithm of Nelder-Mead (NM) to a hybrid optimizer (HO) based on examples.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2012, 6, 1; 42-45
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:
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:
Particle swarm optimization of artificial-neural-network-based on-line trained speed controller for battery electric vehicle
Autorzy:
Ufnalski, B.
Grzesiak, L.
Powiązania:
https://bibliotekanauki.pl/articles/201631.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
electric vehicle
speed control
adaptive ANN controller
particle swarm optimization (PSO)
Opis:
The paper presents implementation of PSO (Particle Swarm Optimization) to ANN-based speed controller tuning. Selected learning parameters are optimized according to the control objective function. A battery electric vehicle is considered as a potential plant for an adaptive speed controller. The need for adaptivity in the control algorithm is justified by variations of a total weight of the vehicle. A sizable section of the paper deals with selection of a combined objective function able to effectively evaluate the quality of a solution.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 661-667
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
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