Wyniki wyszukiwania - Biblioteka Nauki

1

Particle swarm optimization and discrete artificial bee colony algorithms for solving production scheduling problems

100%

Witkowski T.

Technical Sciences / University of Warmia and Mazury in Olsztyn

|

2019

|

tom nr 22(1)

61--74

EN

This paper shows the use of Discrete Artificial Bee Colony (DABC) and Particle Swarm Optimization (PSO) algorithm for solving the job shop scheduling problem (JSSP) with the objective of minimizing makespan. The Job Shop Scheduling Problem is one of the most difficult problems, as it is classified as an NP-complete one. Stochastic search techniques such as swarm and evolutionary algorithms are used to find a good solution. Our objective is to evaluate the efficiency of DABC and PSO swarm algorithms on many tests of JSSP problems. DABC and PSO algorithms have been developed for solving real production scheduling problem too. The experiment results indicate that this problem can be effectively solved by PSO and DABC algorithms.

2

On the efficiency of population-based optimization in finding best parameters for RGB-D visual odometry

100%

Kostusiak A. , Skrzypczyński P.

Journal of Automation Mobile Robotics and Intelligent Systems

|

2019

|

tom Vol. 13, No. 2

5--14

EN

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.

3

A hybrid PSO approach for solving non-convex optimization problems

100%

Ganesan T. , Vasant P. , Elamvazuthy I.

Archives of Control Sciences

|

2012

|

tom Vol. 22, no. 1

87-105

EN

The aim of this paper is to propose an improved particle swarm optimization (PSO) procedure for non-convex optimization problems. This approach embeds classical methods which are the Kuhn-Tucker (KT) conditions and the Hessian matrix into the fitness function. This generates a semi-classical PSO algorithm (SPSO). The classical component improves the PSO method in terms of its capacity to search for optimal solutions in non-convex scenarios. In this work, the development and the testing of the refined the SPSO algorithm was carried out. The SPSO algorithm was tested against two engineering design problems which were; ‘optimization of the design of a pressure vessel’ (P1) and the ‘optimization of the design of a tension/compression spring’ (P2). The computational performance of the SPSO algorithm was then compared against the modified particle swarm optimization (PSO) algorithm of previous work on the same engineering problems. Comparative studies and analysis were then carried out based on the optimized results. It was observed that the SPSO provides a better minimum with a higher quality constraint satisfaction as compared to the PSO approach in the previous work.

4

Comparative Study of Particle Swarm Optimization and Genetic Algorithms for Complex Mathematical Functions

100%

Valdez F. , Melin P.

Journal of Automation Mobile Robotics and Intelligent Systems

|

2008

|

tom Vol. 2, No. 1

43-51

EN

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.

5

Particle swarm optimization for tuning PSS-PID controller of synchronous generator

100%

Derrar A. , Naceri A.

Journal of Automation Mobile Robotics and Intelligent Systems

|

2017

|

tom Vol. 11, No. 1

48--52

EN

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.

6

Particle swarm optimization of artificial-neural-network-based on-line trained speed controller for battery electric vehicle

100%

Ufnalski B. , Grzesiak L.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2012

|

tom Vol. 60, nr 3

661-667

EN

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.

7

Particle swarm optimization of an iterative learning controller for the single-phase inverter with sinusoidal output voltage waveform

100%

Ufnalski B. , Grzesiak L. M. , Gałkowski K.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2013

|

tom Vol. 61, nr 3

649--660

EN

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.

8

Celestial navigation fix based on particle swarm optimization

100%

Tsou M.-C.

Polish Maritime Research

|

2015

|

tom nr 3

20--27

EN

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.

9

Markerless Articulated Human Body Tracking for Gait Analysis and Recognition

100%

Krzeszowski T. , Kwolek B. , Wojciechowski K. , Josinski H.

Machine Graphics and Vision

|

2011

|

tom Vol. 20, No. 3

267-281

EN

We present a particle swarm optimization (PSO) based system for markerless full body motion tracking. The fitness function is smoothed in an annealing scheme and then quantized. In this manner we extract a pool of candidate best particles. The swarm of particles selects a global best from such a pool of the particles to force the PSO the jump out of stagnation. Experiments on 4-camera datasets demonstrate the accuracy of our method on image sequences with walking persons. The system was evaluated using ground-truth data from a marker-based motion capture system by Vicon. We compared the joint motions and the distances between ankles, which were extracted using both systems. Thanks to the high precision of the markerless motion estimation, the curves illustrating the distances between ankles overlap considerably in almost all frames of the image sequences.

10

The multi-constrained multicast routing improved by hybrid bacteria foraging-particle swarm optimization

100%

Sahoo S. P. , Kabat M. R.

Computer Science

|

2019

|

tom Vol. 20 (2)

245--269

EN

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.

11

Vibroacoustic Real Time Fuel Classification in Diesel Engine

100%

Bąkowski A. , Kekez M. , Radziszewski L. , Sapietova A.

Archives of Acoustics

|

2018

|

tom Vol. 43, No. 3

385--395

EN

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.

12

Toward the best combination of optimization with fuzzy systems to obtain the best solution for the GA and PSO algorithms using parallel processing

100%

Valdez F. , Kawano Y. , Melin P.

Journal of Automation Mobile Robotics and Intelligent Systems

|

2020

|

tom Vol. 14, No. 1

55--64

EN

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.

13

A hybrid method for the optimal reactive power dispatch and the control of voltagesin an electrical energy network

100%

Benchabira A. , Khiat M.

Archives of Electrical Engineering

|

2019

|

tom Vol. 68, nr 3

535--551

EN

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.

14

Propeller optimization for small unmanned aerial vehicles

100%

Kusznir T. , Smoczek J.

Journal of KONES

|

2017

|

tom Vol. 24, No. 2

125--132

EN

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.

15

Function optimization using metaheuristics

100%

Pilski M. , Seredyński F.

Studia Informatica : systems and information technology

|

2006

|

tom Vol. 1(7)

77--91

EN

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.

16

An arma type pi-sigma artificial neural network for nonlinear time series forecasting

100%

Akdeniz E. , Egrioglu E. , Bas E. , Yolcu U.

Journal of Artificial Intelligence and Soft Computing Research

|

2018

|

tom Vol. 8, No. 2

121--132

EN

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.

17

Zastosowanie przykładowego algorytmu stadnego w optymalizacji kombinatorycznej

100%

Filipowicz B. , Kwiecień J.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

|

2010

|

tom T. 14, z. 3/2

885-891

PL

Algorytmy metaheurystyczne inspirowane naturą znajdują szerokie zastosowanie w problemach optymalizacji kombinatorycznej. Do tej klasy metod należy algorytm optymalizacji rojem cząstek oparty na zachowaniach stada ptaków. W artykule przedstawiono zastosowanie binarnego algorytmu optymalizacji rojem cząstek do rozwiązania wielowymiarowego problemu plecakowego. Zaprezentowano również wyniki eksperymentów dla wybranych instancji testowych.

EN

Nature-inspired metaheuristic algorithms are successfully applied to combinatorial optimization problems. They incorporate particle swarm optimization inspired by the behaviors of bird flocks. This paper presents the applying of binary particle swarm optimization to the multidimensional knapsack problem. The results of computational experiments for standard test problems have been also presented.

18

Identification of the static recrystallization cellular automata model parameters based on inverse analysis

100%

Sitko M. , Madej Ł. , Kuziak R.

Computer Methods in Materials Science

|

2014

|

tom Vol. 14, No. 4

206--214

EN

Development of the inverse algorithm for identification of the discrete cellular automata model of the static recrystallization based on the particle swarm optimization method is the main aim of the work. First, the idea of the inverse analysis approach is presented. Then subsequent modules of the algorithm are discussed, namely: direct problem model, experimental setup and optimization algorithm. The optimization part is realized by the basic variant of particle swarm optimization (PSO) method. Finally, examples of identified model parameters are presented and obtained results of recrystallized microstructures are compared with the experimental data.

PL

W artykule omówiono identyfikację parametrów modelu automatów komórkowych (Cellular Automata - CA) do symulacji rekrystalizacji statycznej z wykorzystaniem metody roju cząstek (Particle Swarm Optimization). W pierwszej części pracy przedstawiono główne założenia wykorzystanej do identyfikacji metody analizy odwrotnej. Następnie przedstawiono opracowany model automatów komórkowych, inspirowaną naturą metodę optymalizacji oraz omówiono uzyskane dane eksperymentalne. W ostatniej części przedstawione zostały wyniki przeprowadzonego procesu identyfikacji.

19

Collision-free autonomous robot navigation in unknown environments utilizing PSO for path planning

100%

Krell E. , Sheta A. , Balasubramanian A. P. R. , King S. A.

Journal of Artificial Intelligence and Soft Computing Research

|

2019

|

tom Vol. 9, No. 4

267--282

EN

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.

20

Increased Performance of a Hybrid Optimizer for Simulation Based Controller Parameterization

100%

Neugebauer R. , Hipp K. , Hellmich A. , Schlegel H.

Journal of Automation Mobile Robotics and Intelligent Systems

|

2012

|

tom Vol. 6, No. 1

42-45

EN

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.