Wyniki wyszukiwania - BazTech

1

Low-cost small-scale autonomous vehicle

Bogrekci Ismail, Demircioglu Pinar, Goren Mustafa Yasir

Journal of Automation Mobile Robotics and Intelligent Systems

|

2024

|

Vol. 18, No. 1

25--34

EN

A low-cost small-scale autonomous vehicle refers to a self-driving vehicle that is designed to be affordable and suitable for smaller applications or specific purposes. In this study, the firefly algorithm was utilized to address obstacle avoidance challenges in the presence of dynamic or statically positioned uncertain obstacles. The autonomous vehicle successfully reached the intended destination, demonstrating a satisfactory level of accuracy. Regardless of the starting point, the vehicle arrived at the predetermined position within an area measuring 5 meters in diameter. The achievement of such results can be attributed to the cost-effective selection of sensors, utilization of a simple algorithm, and the implementation of a moderately powered processor and circuit components.

2

A methodology for tuning and optimization of a quadruple tank control system using firefly algorithm in wavelet space

Pires André Henrique Matias, Araújo Fábio Meneghetti Ugulino

Przegląd Elektrotechniczny

|

2023

|

R. 99, nr 5

83--89

EN

Using more efficient tuning techniques becomes imperative, due to the increasing competitiveness in the industry. With this propose, meta-heuristics, such as Firefly Algorithm (FA), can be used to obtain the parameters of the controller according to a cost function, which should encode how good a controller is, adequately expressing the desired specifications, so that the metaheuristic employed can find the desired controller that is able to reach the response wanted. The methods traditionally used for automatic tuning of controlers present difficulties in expressing the desired specifications, being able to mapping the desired search space and allowing that the algorithm finds the proper answer. These difficulties is more evident when more complex controllers are required, as for Multiple Input Multiple Output (MIMO) problems. Aiming to solve these difficulties, a methodology using wavelet transform to describe the behavior of a controller response and its use for obtain better performance of the optimization algorithm. A case study will be done using the quadruple tank system, showing the efficiency of the methodology proposed.

PL

Stosowanie bardziej wydajnych technik strojenia staje się koniecznoscią ze względu na rosnącą konkurencyjnosć w branży. Dzięki tej propozycji meta-heurystyki, takie jak Firefly Algorithm (FA), mogą byc użyte do uzyskania parametrów kontrolera zgodnie z funkcją kosztu, która powinna kodowac, jak dobry jest kontroler, adekwatnie wyrażajac poządane specyfikacje, tak aby zastosowana metaheurystyka moze znaleźć ządany kontroler, który jest w stanie osiągnąć ządaną odpowiedź. Metody tradycyjnie stosowane do automatycznego dostrajania sterowników stwarzają trudnosci w wyrażeniu pożądanych specyfikacji, mozliwości odwzorowania pożądanej przestrzeni wyszukiwania i umozliwienia algorytmowi znalezienia ˙ własciwej odpowiedzi. Trudności te są bardziej widoczne, gdy wymagane są bardziej złozone kontrolery, jak w przypadku problemów z wieloma wejściami i wieloma wyjsciami (MIMO). Mając na celu rozwiązanie tych trudnosci, opracowano metodologię wykorzystującą transformat falkową do opisu zachowania się odpowiedzi sterownika i jej zastosowanie w celu uzyskania lepszej wydajnosci algorytmu optymalizacji. Zostanie przeprowadzone ´ studium przypadku z wykorzystaniem systemu poczwórnego zbiornika, pokazujące skuteczność proponowanej metodologii.

3

Synthesis of Reconfigurable Multiple Shaped Beams of a Concentric Circular Ring Array Antenna Using Evolutionary Algorithms

Dubey Sanjay Kumar, Mandal Debasis

Journal of Telecommunications and Information Technology

|

2023

|

nr 1

8--17

EN

The approach described in this paper uses evolutionary algorithms to create multiple-beam patterns for a concentric circular ring array (CCRA) of isotropic antennas using a common set of array excitation amplitudes. The flat top, cosec2, and pencil beam patterns are examples of multiple-beam patterns. All of these designs have an upward angle of θ = 0◦. All the patterns are further created in three azimuth planes (φ = 0◦, 5◦, and 10◦). To create the necessary patterns, non-uniform excitations are used in combination with evenly spaced isotropic components. For the flat top and cosecant-squared patterns, the best combination of common components, amplitude and various phases is applied, whereas the pencil beam pattern is produced using the common amplitude only. Differential evolutionary algorithm (DE), genetic algorithm (GA), and firefly algorithm (FA) are used to generate the best 4-bit discrete magnitudes and 5-bit discrete phases. These discrete excitations aid in lowering the feed network design complexity and the dynamic range ratio (DRR). A variety of randomly selected azimuth planes are used to verify the excitations as well. With small modifications in the desired parameters, the patterns are formed using the same excitation. The results proved both the efficacy of the suggested strategy and the dominance of DE over GA as well as FA.

4

Finding playing styles of badminton players using firefly algorithm-based clustering algorithms

Ilankoon I.M.T.P.K., Samarasinghe U.S., Ariyaratne M.K.A., Silva R. M.

Computer Science

|

2023

|

T. 24 (3)

427--450

EN

Cluster analysis can be defined as applying clustering algorithms with the goal of finding any hidden patterns or groupings in a data set. Different clustering methods may provide different solutions for the same data set. Traditional clustering algorithms are popular, but handling big data sets is beyond the abilities of such methods. We propose three big data clustering methods basedon the firefly algorithm (FA). Three different fitness functions were definedon FA using inter-cluster distance, intra-cluster distance, silhouette value, and the Calinski-Harabasz index. The algorithms find the most appropriate cluster centers for a given data set. The algorithms were tested with nine popular synthetic data sets and one medical data set and are later applied on two badminton data sets with the intention of identifying the different playing styles of players based on their physical characteristics. The results specify that the firefly algorithm could generate better clustering results with high accuracy. The algorithms cluster the players to find the most suitable playing strategy for a given player where expert knowledge is needed in labeling the clusters. Comparisons with a PSO-based clustering algorithm (APSO) and traditional algorithms point out that the proposed firefly variants work in a similar fashion as the APSO method, and they surpass the performance of traditional algorithms.

5

Firefly algorithm optimization of manipulator robotic control based on fast terminal sliding mode

Mallem Ali, Douak Fouzi, Benaziza Walid, Bounouara Asma

Journal of Automation Mobile Robotics and Intelligent Systems

|

2022

|

Vol. 16, No. 4

44--52

EN

In this paper a new algorithm of optimization in the field of manipulator robotic control is presented. The proposed control approach is based on fast terminal sliding mode control (FTSMC), in order to guarantee the convergence of the position articulations errors to zero in finite time without chattering phenomena, and the Firefly algorithm in order to generate the optimal parameters that ensure minimum reaching time and mean square error and achieve better performances. This ensures the asymptotic stability of the system using a Lyapunov candidate in the presence of disturbances. The simulations are applied on a two-link robotic manipulator with different tracking references by using Matlab/ Simulink. Results show the efficiency and confirm the robustness of the proposed control strategy.

6

A comprehensive study on the application of firefly algorithm in prediction of energy dissipation on block ramps

Mahdavi-Meymand Amin, Sulisz Wojciech, Zounemat-Kermani Mohammad

Eksploatacja i Niezawodność

|

2022

|

Vol. 24, no. 2

200--210

EN

In this study novel integrative machine learning models embedded with the firefly algorithm (FA) were developed and employed to predict energy dissipation on block ramps. The used models include multi-layer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), support vector regression (SVR), linear equation (LE), and nonlinear regression equation (NE). The investigation focused on the evaluation of the performance of standard and integrative models in different runs. The performances of machine learning models and the nonlinear equation are higher than the linear equation. The results also show that FA increases the performance of all applied models. Moreover, the results indicate that the ANFIS-FA is the most stable integrative model in comparison to the other embedded methods and reveal that GMDH and SVR are the most stable technique among all applied models. The results also show that the accuracy of the LE-FA technique is relatively low, RMSE=0.091. The most accurate results provide SVR-FA, RMSE=0.034.

7

Gravity inversion of basement relief using imperialist competitive algorithm with hybrid techniques

Joolaei Amir, Arab Amiri-Alireza, Nejati Ali

Acta Geophysica

|

2022

|

Vol. 70, no 6

2629--2646

EN

Classically, local deterministic optimization techniques have been employed to solve such nonlinear gravity inversion problem. Nevertheless, local search methods can also be easily implemented and demonstrate higher rates of convergence; but in highly nonlinear cases such as geophysical problems, they require a reliable initial model which should be adequately close to the true model. Recently, global optimization methods have shown promising results as an alternative to classical inversion methods. Each of the global optimization algorithms has unique benefits and faults; therefore, applying different combinations of them is one of the proposed solutions for overcoming their distinct limitations. In this research, the design and implementation of the hybrid method based on a combination of the imperialist competitive algorithm (ICA) and firefly algorithm (FA) as tools of two-dimensional nonlinear modeling of gravity data and as a substitute for the local optimization methods were investigated. Hybrid of ICA and FA algorithm (known as ICAFA) is a modified form of the ICA algorithm based on the firefly algorithm. This modification results in an increase in the exploratory capability of the algorithm and improvement of its convergence rate. This inversion technique was first successfully tested on a synthetic gravity anomaly originated from a simulated sedimentary basin model both with and without the presence of white Gaussian noise (WGN). At last, the method was applied to the Bouguer anomaly from a real gravity profile in Moghan sedimentary basin (Iran). The results of this modeling were compatible with previously published works which consisted of both seismic analysis and other gravity interpretations. In order to estimate the uncertainty of solutions, several inversion runs were also conducted independently and the results were in line with the final solution.

8

Wykorzystanie algorytmu świetlika dla znajdowania ekstremów globalnych wybranych funkcji testowych

Olszewski Jan, Figielska Ewa

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

|

2021

|

nr 24

7--34

PL

W artykule przedstawiona została jedna z najnowszych metod inteligencji rojowej – algorytm świetlikowy zaproponowany przez Xin-She Yanga w roku 2008. Przeprowadzona została analiza działania algorytmu, zbadany został wpływ zmian wartości jego parametrów na jakość uzyskiwanych rozwiązań przy poszukiwaniu ekstremów globalnych wybranych nieliniowych funkcji jedno i wielomodalnych.

EN

The paper presents one of the newest swarm intelligence methods, namely firefly algorithm proposed by Xin-She Yang in 2008. The analysis of the per- formance of the algorithm is carried out and the influence of the algorithm parameters settings on the quality of the solutions is examined using nonlinear single and multi-modal mathematical test functions.

9

Firefly algorithm applied to the job-shop scheduling problem

Świtalski Piotr, Bolesta Arkadiusz

Studia Informatica : systems and information technology

|

2021

|

Vol. 1-2(25)

87--100

EN

The job shop scheduling problem (JSSP) is one of the most researched scheduling problems. This problem belongs to the NP-hard class. An optimal solution for this category of problems is rarely possible. We try to find suboptimal solutions using heuristics or metaheuristics. The firefly algorithm is a great example of a metaheuristic. In this paper, this algorithm is used to solve JSSP. We used some benchmarking JSSP datasets for experiments. The experimental program was implemented in the aitoa library. We investigated the optimal parameter settings of this algorithm in terms of JSSP. Analysis of the experimental results shows that the algorithm is useful to solve scheduling problems.

10

Neural network-based allocation and self-improved firefly-based optimal sizing of fuel cells in distributed generation systems

Subramanyam T. C., Tulasi Ram S. S., Subrahmanyam J. B. V.

Control and Cybernetics

|

2018

|

Vol. 47, no. 4

357--381

EN

The notion of Distributed Generation (DG) refers to the production of power at the level of consumption. Production of energy on-site, instead of offering it centrally, reduces the cost, internal dependencies, difficulties, inefficiencies, and risks that are related to transmission and distribution systems. In case DG is realized with fuel cells, several issues exist in respect to allocating and sizing of these fuel cells in the system. For solving those issues, dual stage intelligent technique is employed in this paper. First, the Neural Networks (NN) technique is adopted for determining the required location to place the fuel cells. Secondly, an enhanced version of Self Improved Fire-Fly (SIFF) algorithm is adopted for finding the optimal size of the fuel cells. The implemented technique is simulated in four IEEE benchmark test bus systems, and the respective performance analysis along with statistical analysis serves for validation purposes. The here proposed technique is compared with six other known algorithms, namely Particle Swarm Optimization (PSO), Firefly (FF) algorithm, Artificial Bee colony (ABC) algorithm, Improved Artificial Bee colony algorithm (IABC), Genetic Algorithm (GA) and Global Search Optimizer (GSO). The results obtained from the comparative analysis show the enhanced performance of the proposed mechanism.

11

Analiza wybranych rojowych algorytmów optymalizacji w zagadnieniach planowania sieci WLAN

Pieprzycki A., Ludwin W.

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

|

2017

|

nr 6

307--310, CD

PL

Celem artykułu było sprawdzenie i porównanie metod optymalizacji inspirowanych naturą w zadaniu planowania sieci łączności bezprzewodowej. Analizie poddano algorytmy rojowe, a uzyskane za ich pomocą wyniki porównano z wynikami modelu empirycznego.

EN

The aim of this article was to examine and compare optimization methods inspired by nature in the task of planning wireless networks. Analyzed swarm algorithms, and obtained numerical results were compared with the results of empirical model as well.

12

Synthesis and Failure Correction of Flattop and Cosecant Squared Beam Patterns in Linear Antenna Arrays

Patidar H., Mahanti G. K., Muralidharan R.

Journal of Telecommunications and Information Technology

|

2017

|

nr 4

25--30

EN

This paper deals with the synthesis of ﬂattop and cosecant squared beam patterns using the ﬁreﬂy algorithm which is based on metaheuristics. This synthesis is followed by the correction of the radiation patterns when unfortunate malfunctioning of the individual elements in the array occurs. The necessary attention is given to the recovery process, with due emphasis on reduction of side lobe level, ripple and the reﬂection coeﬃcient. Simulation in Matlab shows a successful employment of the ﬁreﬂy algorithm in producing voltage excitations of the good elements necessary for the recovered patterns. The performance of the ﬁreﬂy algorithm in failure correction is validated by duly comparing it with a standard benchmark.

13

Optimization of complex systems reliability by firefly algorithm

Kwiecień J., Filipowicz B.

Eksploatacja i Niezawodność

|

2017

|

Vol. 19, no. 2

296--301

PL

Algorytmy bazujące na inteligencji stadnej są coraz częściej stosowane w problemach niezawodności systemów. Artykuł prezentuje zastosowanie algorytmu świetlika do optymalizacji niezawodności dwóch systemów: mostkowego i 10-elementowego, z wykorzystaniem metod zbioru minimalnych ścieżek, minimalnych cięć oraz metody dekompozycji. Uzyskane rezultaty zostały przedstawione i porównane z dostępnymi danymi literaturowymi.

EN

Algorithms based on swarm intelligence are more and more frequently applied to problems of systems reliability. The article presents the application of a firefly algorithm to the reliability optimization of two systems: bridge and 10-unit, with minimal paths set, minimal cuts set and decomposition methods. The obtained results are presented and compared with the available literature data.

14

Artifacts removal from EEG signal: FLM optimization-based learning algorithm for neural network-enhanced adaptive filtering

Quazi M. H., Kahalekar S. G.

Biocybernetics and Biomedical Engineering

|

2017

|

Vol. 37, no. 3

401--411

EN

Electroencephalogram (EEG) denotes a neurophysiologic measurement, which observes the electrical activity of the brain through making a record of the EEG signal from the electrodes positioned on the scalp. The EEG signal gets mixed with other biological signals, called artifacts. Few artifacts include electromyogram (EMG), electrocardiogram (ECG) and electrooculogram (EOG). Removal of artifacts from the EEG signal poses a great challenge in the medical field. Hence, the FLM (Firefly + Levenberg Marquardt) optimization-based learning algorithm for neural network-enhanced adaptive filtering model is introduced to eliminate the artifacts from the EEG. Initially, the EEG signal was provided to the adaptive filter for yielding the optimal weights using the renowned optimization algorithms, called firefly algorithm and LM. These two algorithms are effectively hybridized and applied to the neural network to find the optimal weights for adaptive filtering. Then, the designed filtering process renders an improved system for artifacts removal from the EEG signal. Finally, the performance of the proposed model and the existing models regarding SNR, computation time, MSE and RMSE are analyzed. The results conclude that the proposed method achieves a high SNR of 42.042 dB.

15

Algorytmy stadne w optymalizacji strukturalnej systemów niezawodnościowych

Kwiecień J., Filipowicz B.

Przegląd Elektrotechniczny

|

2014

|

R. 90, nr 6

195--197

PL

W artykule przedstawiono zastosowanie algorytmów pszczelich i świetlika do wyznaczenia optymalnej struktury serwisu technicznego, w celu zapewnienia jego niezawodności oraz zminimalizowania kosztów związanych z jego funkcjonowaniem. Przedstawiono i porównano wyniki badań dla wybranych wygenerowanych problemów.

EN

This paper present the use of bee algorithms and firefly algorithm to determine the optimal structure of technical service, in order to ensure its reliability and to minimize the costs associated with its operation. The results of experiments for generated test instances are presented.

16

Comparison of firefly and cockroach algorithms in selected discrete and combinatorial problems

Kwiecień J., Filipowicz B.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2014

|

Vol. 62, nr 4

797--804

EN

In recent years, newer algorithms inspired by nature have been created and used to solve various problems. Therefore, in the paper we present the application of firefly and cockroach algorithms to optimize two queueing systems and permutation flow shop problems with the objective of minimizing the makespan. The article briefly describes these algorithms to solve selected problems and their results. Because these algorithms were originally developed for continuous optimization problems, we introduce a new formula to transform the position of ith individual to solve the discrete problems.

17

Application of the firefly algorithm to optimize the fuzzy controller settings in the overhead crane

Kawlewski K.

Modelling and Optimization of Physical Systems

|

2013

|

z. 12

39--42

EN

This paper presents use of firefly algorithm to optimize pararneters of crane fuzzy logic controller to reduce the swing of payload. The results obtained during the optimization were compared with the results obtained using genetic algorithms. Has been shown a high efficiency and speed in achieving the optimal values of the fuzzy logic controller parameters based on the optimization process using firefly algorithm.

PL

W artykule przedstawiono zastosowanie algorytmu świetlika do optymalizacji nastaw regulatora rozmytego suwnicy w celu zmniejszenia kołysania ładunku. Wyniki otrzymane podczas optymalizacji porównano z rezultatami uzyskanymi za pomocą algorytmów ewolucyjnych. Wykazano dużą efektywność oraz szybkość w uzyskiwaniu wartości nastaw w oparciu o proces optymalizacji za pomocą algorytmu świetlika.

18

Firefly algorithm in optimization of queueing systems

Kwiecień J., Filipowicz B.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2012

|

Vol. 60, nr 2

363-368

EN

Queueing theory provides methods for analysis of complex service systems in computer systems, communications, transportation networks and manufacturing. It incorporates Markovian systems with exponential service times and a Poisson arrival process. Two queueing systems with losses are also briefly characterized. The article describes firefly algorithm, which is successfully used for optimization of these queueing systems. The results of experiments performed for selected queueing systems have been also presented.

19

Algorytmy stadne w problemach optymalizacji

Filipowicz B., Kwiecień J.

Pomiary Automatyka Robotyka

|

2011

|

R. 15, nr 12

152-157

PL

W artykule przedstawiono zastosowanie algorytmu optymalizacji rojem cząstek, algorytmu pszczelego i algorytmu świetlika do wyznaczenia optymalnego rozwiązania wybranych testowych funkcji ciągłych. Przedstawiono i porównano wyniki badań dla funkcji Rosenbrocka, Rastrigina i de Jonga.

EN

This paper presents particle swarm optimization, bee algorithm and firefly algorithm, used for optimal solution of selected continuous well-known functions. Results of these algorithms are compared to each other on Rosenbrock, Rastrigin and de Jong functions.