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Tytuł:
Computer-aided system for layout of fire hydrants on boards designed vessel using the Particle Swarm Optimization algorithm
Autorzy:
Gomułka, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/34600515.pdf
Data publikacji:
2023
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
ship
fire hydrant
design
layout
particle swarm optimization
Opis:
The functional layout of fire safety equipment in technical spaces of ships is a time-consuming process. When designing a ship fire protection system, the designer must manually position each system component in such a way as to meet the requirements of regulations arising from the technical specification, various legal regulations of maritime conventions and classification societies of the vessel to be designed. Layout of fire hydrants assisted by a computer that is based on pre-defined criteria and various constraints could significantly support the designer in working easier and faster. This paper presents a prototype computer-aided design system that enables optimal placement of fire hydrants using the metaheuristic Particle Swarm Optimization (PSO) algorithm. This algorithm was used in Rhinoceros 3D software with its Grasshopper plugin for visualizing the arrangement of fire safety equipment. Various solution arrangements compared with the fire hydrant placement in real ships are illustrated by a case study. Demonstrating how design work can be facilitated and what potential benefits can be achieved are presented as well.
Źródło:
Polish Maritime Research; 2023, 4; 4-16
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm
Autorzy:
Tian, Niu
Huang, Songwei
He, Lifang
Du, Lingpan
Yang, Sheping
Huang, Bin
Powiązania:
https://bibliotekanauki.pl/articles/24085898.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
fuzzy control
contraction-expansion factor
particle swarm optimization
Opis:
The grinding process in the concentrator is a part of the largest energy consumption, but also the most likely to cause a waste of resources, so the optimization of the grinding process is a very important link.The traditional fuzzy controller relies solely on the expert knowledge summary to construct control rules, which can cause significant steady-state errors in the model. In order to solve the above problem, this paper proposes an elastic variable universe fuzzy control based on Particle Swarm Optimization (PSO) algorithm. The elastic universe fuzzy control model does not need precise fuzzy rules, but only needs to input the general trend of the rules, and the division of the universe is performed by the contraction-expansionfactor. The control performance is directly related to the contraction-expansionfactor, so this article also proposes using particle swarm optimization to optimize the scaling factor to achieve the optimal value. Finally, simulation models of traditional fuzzy control and elastic universe fuzzy control of feeding system of mill were built using Python to verify the control effect. Itssimulation results show that the time of the reaction of the fuzzy control system in the elastic variable theory universe based on particle swarm optimization was shorter by 34.48% comparing to the traditional one. Elastic variable universe fuzzy control based on particle swarm optimization (PSO) effectively improved the control accuracy of the mill feeding system and improved the response speed of the system to a certain extent.
Źródło:
Physicochemical Problems of Mineral Processing; 2023, 59, 3; art. no. 169942
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid PSO-GA algorithm for Reversible Circuits Synthesis
Hybrydowy algorytm PSO-GA dla syntezy układów odwracalnych
Autorzy:
Podlaski, K.
Powiązania:
https://bibliotekanauki.pl/articles/153468.pdf
Data publikacji:
2014
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
reversible circuits
reversible logic synthesis
particle swarm optimization (PSO)
genetic algorithms
układy odwracalne
synteza układów odwracalnych
particle swarm optimization
algorytmy genetyczne
Opis:
In the domain of Reversible Circuits there is still lack of good synthesis algorithms. There are many heuristic propositions, unfortunately, their results for a given reversible function usually are circuits far from optimal implementations. There are some propositions of using Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) for this purpose. In this paper a new hybrid PSO-GA algorithm is proposed. Comparison of the proposed algorithm with the existing ones gives promising results.
W dobie poszukiwania układów cyfrowych o niskim zużyciu energii układy odwracalne stanowią ciekawą alternatywę dla aktualnie stosowanych układów cyfrowych. Jednym z najistotniejszych zagadnień w dziedzinie budowy układów cyfrowych jest synteza układu reprezentującego zadaną funkcję. Niestety do dzisiaj nie ma dobrych rozwiązań w dziedzinie syntezy układów odwracalnych, istniejące rozwiązania są bardzo czasochłonne bądź generują układy o dużej redundancji. Ciekawą alternatywą dla obecnie stosowanych metod heurystycznych jest wykorzystanie algorytmów ewolucyjnych np. Particle Swarm Optimization (PSO) lub algorytmów genetycznych (GA). W niniejszym artykule zaproponowano nowy hybrydowy algorytm PSO-GA dostosowany do syntezy odwracalnych układów cyfrowych. Stworzony algorytm zastosowano do syntezy układów dla wybranych funkcji testowych (tzw. benchmarków) a wyniki porównano z wynikami otrzymywanymi za pomocą algorytmów heurystycznych. Wygenerowane układy okazały się mniej redundantne niż układy otrzymane w syntezie metodami heurystycznymi.
Źródło:
Pomiary Automatyka Kontrola; 2014, R. 60, nr 7, 7; 474-476
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fixing Design Inconsistencies of Polymorphic Methods Using Swarm Intelligence
Autorzy:
George, Renu
Samuel, Philip
Powiązania:
https://bibliotekanauki.pl/articles/1818478.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
UML models
software design inconsistency
polymorphism
particle swarm optimization
Opis:
Background: Modern industry is heavily dependent on software. The complexity of designing and developing software is a serious engineering issue. With the growing size of software systems and increase in complexity, inconsistencies arise in software design and intelligent techniques are required to detect and fix inconsistencies. Aim: Current industrial practice of manually detecting inconsistencies is time consuming, error prone and incomplete. Inconsistencies arising as a result of polymorphic object interactions are hard to trace. We propose an approach to detect and fix inconsistencies in polymorphic method invocations in sequence models. Method: A novel intelligent approach based on self regulating particle swarm optimization to solve the inconsistency during software system design is presented. Inconsistency handling is modelled as an optimization problem that uses a maximizing fitness function. The proposed approach also identifies the changes required in the design diagrams to fix the inconsistencies. Result: The method is evaluated on different software design models involving static and dynamic polymorphism and inconsistencies are detected and resolved. Conclusion: Ensuring consistency of design is highly essential to develop quality software and solves a major design issue for practitioners. In addition, our approach helps to reduce the time and cost of developing software.
Źródło:
e-Informatica Software Engineering Journal; 2021, 15, 1; 7--27
1897-7979
Pojawia się w:
e-Informatica Software Engineering Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Coordinated control strategy for microgrid stability maintenance under isolated island operation
Autorzy:
Wu, Pan
Xu, Xiaowei
Powiązania:
https://bibliotekanauki.pl/articles/1841281.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
coordinated control
isolated island operation
microgrid
particle swarm optimization
Opis:
In this study, the inverter in a microgrid was adjusted by the particle swarm optimization (PSO) based coordinated control strategy to ensure the stability of the isolated island operation. The simulation results showed that the voltage at the inverter port reduced instantaneously, and the voltage unbalance degree of its port and the port of point of common coupling (PCC) exceeded the normal standard when the microgrid entered the isolated island mode. After using the coordinated control strategy, the voltage rapidly recovered, and the voltage unbalance degree rapidly reduced to the normal level. The coordinated control strategy is better than the normal control strategy.
Źródło:
Archives of Electrical Engineering; 2021, 70, 2; 285-295
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Power system oscillation damping controller design: a novel approach of integrated HHO-PSO algorithm
Autorzy:
Devarapalli, Ramesh
Kumar, Vikash
Powiązania:
https://bibliotekanauki.pl/articles/1845528.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Harris hawk optimization
power system stabilizers
STATCOM
FACTS
particle swarm optimization
Opis:
The hybridization of a recently suggested Harris hawk’s optimizer (HHO) with the traditional particle swarm optimization (PSO) has been proposed in this paper. The velocity function update in each iteration of the PSO technique has been adopted to avoid being trapped into local search space with HHO. The performance of the proposed Integrated HHO-PSO (IHHOPSO) is evaluated using 23 benchmark functions and compared with the novel algorithms and hybrid versions of the neighbouring standard algorithms. Statistical analysis with the proposed algorithm is presented, and the effectiveness is shown in the comparison of grey wolf optimization (GWO), Harris hawks optimizer (HHO), barnacles matting optimization (BMO) and hybrid GWO-PSO algorithms. The comparison in convergence characters with the considered set of optimization methods also presented along with the boxplot. The proposed algorithm is further validated via an emerging engineering case study of controller parameter tuning of power system stability enhancement problem. The considered case study tunes the parameters of STATCOM and power system stabilizers (PSS) connected in a sample power network with the proposed IHHOPSO algorithm. A multi-objective function has been considered and different operating conditions has been investigated in this papers which recommends proposed algorithm in an effective damping of power network oscillations.
Źródło:
Archives of Control Sciences; 2021, 31, 3; 553-591
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Power system oscillation damping controller design: a novel approach of integrated HHO-PSO algorithm
Autorzy:
Devarapalli, Ramesh
Kumar, Vikash
Powiązania:
https://bibliotekanauki.pl/articles/1845539.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Harris hawk optimization
power system stabilizers
STATCOM
FACTS
particle swarm optimization
Opis:
The hybridization of a recently suggested Harris hawk’s optimizer (HHO) with the traditional particle swarm optimization (PSO) has been proposed in this paper. The velocity function update in each iteration of the PSO technique has been adopted to avoid being trapped into local search space with HHO. The performance of the proposed Integrated HHO-PSO (IHHOPSO) is evaluated using 23 benchmark functions and compared with the novel algorithms and hybrid versions of the neighbouring standard algorithms. Statistical analysis with the proposed algorithm is presented, and the effectiveness is shown in the comparison of grey wolf optimization (GWO), Harris hawks optimizer (HHO), barnacles matting optimization (BMO) and hybrid GWO-PSO algorithms. The comparison in convergence characters with the considered set of optimization methods also presented along with the boxplot. The proposed algorithm is further validated via an emerging engineering case study of controller parameter tuning of power system stability enhancement problem. The considered case study tunes the parameters of STATCOM and power system stabilizers (PSS) connected in a sample power network with the proposed IHHOPSO algorithm. A multi-objective function has been considered and different operating conditions has been investigated in this papers which recommends proposed algorithm in an effective damping of power network oscillations.
Źródło:
Archives of Control Sciences; 2021, 31, 3; 553-591
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Performance Comparison of Optimization Methods for Flat-Top Sector Beamforming in a Cellular Network
Autorzy:
Nandi, Pampa
Roy, Jibendu Sekhar
Powiązania:
https://bibliotekanauki.pl/articles/2142316.pdf
Data publikacji:
2022
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
flat-top sector beam
particle swarm optimization
real-coded genetic algorithm
Opis:
The flat-top radiation pattern is necessary to form an appropriate beam in a sectored cellular network and to pro vide users with best quality services. The flat-top pattern offers sufficient power and allows to minimize spillover of signal to adjacent sectors. The flat-top sector beam pattern is relied upon In sectored cellular networks, in multiple-input multiple-output (MIMO) systems and ensures a nearly constant gain in the desired cellular sector. This paper presents a comparison of such optimization techniques as real-coded genetic algorithm (RGA) and particle swarm optimization (PSO), used in cellular networks in order to achieve optimum flat-top sector patterns. The individual parameters of flat-top sector beams, such as cellular coverage, ripples in the flat-top beam, spillover of radiation to the adjacent sectors and side lobe level (SLL) are investigated through optimization performed for 40◦ and 60◦ sectors. These parameters are used to compare the performance of the optimized RGA and PSO algorithms. Overall, PSO outperforms the RGA algorithm.
Źródło:
Journal of Telecommunications and Information Technology; 2022, 3; 39--46
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of a particle swarm optimization to a physically-based erosion model
Zastosowanie optymalizacji zbioru rozproszonych czasteczek do modelu erozji opartego na podstawach fizycznych
Autorzy:
Santos, C A G
Pinto, L.E.M.
De Macedo Machado Freire, P.K.
Mishra, S.K.
Powiązania:
https://bibliotekanauki.pl/articles/81761.pdf
Data publikacji:
2010
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
particle swarm optimization
application
erosion model
erosion simulation
run-off
optimization
erosion parameter
Opis:
The difficulties involved in calibration of physically based erosion models have been partly attributable to the lack of robust optimization tools. This paper presents the essential concepts and application to optimize channel and plane parameters in an erosion model, with a global optimization method known as Repulsive Particle Swarm (RPS), a variant of Particle Swarm Optimization (PSO) method. The physically-based erosion model that which was chosen is called WESP (watershed erosion simulation program). The optimization technique was tested with the field data collected in an experimental watershed located in a semi-arid region of Brazil. On the basis of these results, the recommended erosion parameter values for a semi-arid region are given, which could serve as an initial estimate for other similar areas.
Trudności w kalibracji modeli erozji opartych na podstawach fizycznych przyczyniły się do braku ogólnych narzędzi optymalizacji. W pracy przedstawiono podstawowe koncepcje i zastosowanie do zoptymalizowania parametrów kanału i płaszczyzny w modelu erozji, globalną metodą optymalizacji znaną jako Repulsive Particle Swarm (RPS), wariantem metody Particle Swarm Optimisation (SPO). Fizycznie uzasadniony model erozji, który został wybrany jest nazywany WESP (watershed erosion simulation program). Technika optymalizacji została wypróbowana na danych terenowych zebranych w zlewni eksperymentalnej zlokalizowanej w umiarkowanie suchym regionie Brazylii. Na podstawie tych wyników przedstawiono wartości rekomendowanego parametru erozji dla umiarkowanie suchego regionu, który może służyć jako początkowe oszacowanie dla podobnych obszarów.
Źródło:
Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation; 2010, 42, 1; 39-49
0208-5771
Pojawia się w:
Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Parameter estimation of photovoltaic module relied on golden jackal optimization
Autorzy:
Nguyen, Thuan Thanh
Powiązania:
https://bibliotekanauki.pl/articles/27309949.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
golden jackal optimization
henry gas solubility optimization
particle swarm optimization
PV parameter estimation
single diode model
Opis:
Due to the nonlinear current-voltage (I-V) relationship of the photovoltaic (PV) module, building a precise mathematical model of the PV module is necessary for evaluating and optimizing the PV systems. This paper proposes a method of building PV parameter estimation models based on golden jackal optimization (GJO). GJO is a recently developed algorithm inspired by the idea of the hunting behavior of golden jackals. The explored and exploited searching strategies of GJO are built based on searching for prey as well as harassing and grabbing prey of golden jackals. The performance of GJO is considered on the commercial KC200GT module under various levels of irradiance and temperature. Its performance is compared to well-known particle swarm optimization (PSO), recent Henry gas solubility optimization (HGSO) and some previous methods. The obtained results show that GJO can estimate unknown PV parameters with high precision. Furthermore, GJO can also provide better efficiency than PSO and HGSO in terms of statistical results over several runs. Thus, GJO can be a reliable algorithm for the PV parameter estimation problem under different environmental conditions.
Źródło:
Archives of Electrical Engineering; 2023, 72, 4; 987--1003
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosis
Autorzy:
Ahsan, Muhammad
Bismor, Dariusz
Manzoor, Muhammad Arslan
Powiązania:
https://bibliotekanauki.pl/articles/27322619.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
signal-to-noise ratio
asymmetric real Laplace wavelet
bandpass filter
particle swarm optimization
spectral kurtosis
fault frequency
Opis:
Rotating element bearings are the backbone of every rotating machine. Vibration signals measured from these bearings are used to diagnose the health of the machine, but when the signal-to-noise ratio is low, it is challenging to diagnose the fault frequency. In this paper, a new method is proposed to enhance the signal-to-noise ratio by applying the Asymmetric Real Laplace wavelet Bandpass Filter (ARL-wavelet-BPF). The Gaussian function of the ARL-wavelet represents an excellent BPF with smooth edges which helps to minimize the ripple effects. The bandwidth and center frequency of the ARL-wavelet-BPF are optimized using the Particle Swarm Optimization (PSO) algorithm. Spectral kurtosis (SK) of the envelope spectrum is employed as a fitness function for the PSO algorithm which helps to track the periodic spikes generated by the fault frequency in the vibration signal. To validate the performance of the ARL-wavelet-BPF, different vibration signals with low signal-to-noise ratio are used and faults are diagnosed.
Źródło:
Archives of Control Sciences; 2023, 33, 3; 589--606
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimizing the Bit-flipping Method for Decoding Low-density Parity-check Codes in Wireless Networks by Using the Artificial Spider Algorithm
Autorzy:
Ghaffoori, Ali Jasim
Abdul-Adheem, Wameedh Riyadh
Powiązania:
https://bibliotekanauki.pl/articles/2055251.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
low-density parity-check
LDPC
hard-decision Bit-Flipping
BF
particle swarm optimization
PSO
artificial spider algorithm
ASA
Opis:
In this paper, the performance of Low-Density Parity-Check (LDPC) codes is improved, which leads to reduce the complexity of hard-decision Bit-Flipping (BF) decoding by utilizing the Artificial Spider Algorithm (ASA). The ASA is used to solve the optimization problem of decoding thresholds. Two decoding thresholds are used to flip multiple bits in each round of iteration to reduce the probability of errors and accelerate decoding convergence speed while improving decoding performance. These errors occur every time the bits are flipped. Then, the BF algorithm with a low-complexity optimizer only requires real number operations before iteration and logical operations in each iteration. The ASA is better than the optimized decoding scheme that uses the Particle Swarm Optimization (PSO) algorithm. The proposed scheme can improve the performance of wireless network applications with good proficiency and results. Simulation results show that the ASA-based algorithm for solving highly nonlinear unconstrained problems exhibits fast decoding convergence speed and excellent decoding performance. Thus, it is suitable for applications in broadband wireless networks.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 1; 109--114
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Particle swarm optimization of a neural network model for predicting the flashover voltage on polluted cap and pin insulator
Autorzy:
Belkebir, Amel
Bourek, Yacine
Benguesmia, Hani
Powiązania:
https://bibliotekanauki.pl/articles/2146737.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
flashover voltage
particle swarm optimization
prediction
artificial pollution
neural network
napięcie przeskoku
optymalizacja roju cząstek
prognozowanie
sieć neuronowa
Opis:
This paper proposes training an artificial neural network (ANN) by a particle swarm optimization (PSO) technique to predict the flashover voltage of outdoor insulators. The analysis follows a series of real-world tests on high-voltage insulators to form a database for implementing artificial intelligence concepts. These tests are performed in various degrees of artificial contamination (distilled brine). Each contamination level shows the amount of contamination in milliliters per area of the isolator. The acquisition database provides values of flashover voltage corresponding to their electrical conductivity in each isolation zone and different degrees of artificial contamination. The results show that ANN trained by PSO can not only provide better prediction results, but also reduce the amount of computation efforts. It is also a more powerful model because: it does not get stuck in a local optimum. In addition, it also has the advantages of simple logic, simple implementation, and underlying intelligence. Compared to the results obtained by practical tests, the results obtained present that the PSO-ANN technique is very effective to predict flashover of high-voltage polluted insulators.
Źródło:
Diagnostyka; 2022, 23, 3; art. no 2022309
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The application of different optimization techniques and Artificial Neural Networks (ANN) for coal-consumption forecasting: a case study
Zastosowanie różnych technik optymalizacji i sztucznych sieci neuronowych (SSN) do prognozowania zużycia węgla: studium przypadku
Autorzy:
Seker, Mustafa
Unal Kartal, Neslihan
Karadirek, Selin
Gulludag, Cevdet Bertan
Powiązania:
https://bibliotekanauki.pl/articles/2173847.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN
Tematy:
coal consumption
metaheuristic optimization
grey wolf optimization
particle swarm optimization
whale optimization
zużycie węgla
optymalizacja metaheurystyczna
optymalizacja szarego wilka
optymalizacja roju cząstek
optymalizacja wielorybów
Opis:
The demand for energy on a global scale increases day by day. Unlike renewable energy sources, fossil fuels have limited reserves and meet most of the world’s energy needs despite their adverse environmental effects. This study presents a new forecast strategy, including an optimization-based S-curve approach for coal consumption in Turkey. For this approach, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) are among the meta-heuristic optimization techniques used to determine the optimum parameters of the S-curve. In addition, these algorithms and Artificial Neural Network (ANN) have also been used to estimate coal consumption. In evaluating coal consumption with ANN, energy and economic parameters such as installed capacity, gross generation, net electric consumption, import, export, and population energy are used for input parameters. In ANN modeling, the Feed Forward Multilayer Perceptron Network structure was used, and Levenberg-Marquardt Back Propagation has used to perform network training. S-curves have been calculated using optimization, and their performance in predicting coal consumption has been evaluated statistically. The findings reveal that the optimization-based S-curve approach gives higher accuracy than ANN in solving the presented problem. The statistical results calculated by the GWO have higher accuracy than the PSO, WOA, and GA with R2 = 0.9881, RE = 0.011, RMSE = 1.079, MAE = 1.3584, and STD = 1.5187. The novelty of this study, the presented methodology does not need more input parameters for analysis. Therefore, it can be easily used with high accuracy to estimate coal consumption within other countries with an increasing trend in coal consumption, such as Turkey.
Zapotrzebowanie na energię w skali globalnej rośnie z dnia na dzień. W przeciwieństwie do odnawialnych źródeł energii, paliwa kopalne mają ograniczone rezerwy i zaspokajają większość światowego zapotrzebowania na energię pomimo ich niekorzystnego wpływu na środowisko. Niniejsze opracowanie przedstawia nową strategię prognozowania, w tym oparte na optymalizacji podejście oparte na krzywej S dla zużycia węgla w Turcji. W tym podejściu algorytmy optymalizacji genetycznej (GA) i optymalizacji roju cząstek (PSO), optymalizacja Gray Wolf (GWO) i algorytm optymalizacji wielorybów (WOA) należą do metaheurystycznych technik optymalizacji stosowanych do określenia optymalnych parametrów krzywej S. Ponadto algorytmy te oraz sztuczna sieć neuronowa (SSN) zostały również wykorzystane do oszacowania zużycia węgla. Przy ocenie zużycia węgla za pomocą SSN jako parametry wejściowe wykorzystuje się parametry energetyczne i ekonomiczne, takie jak moc zainstalowana, produkcja brutto, zużycie energii elektrycznej netto, import, eksport i energia ludności. W modelowaniu SSN wykorzystano strukturę Feed Forward Multilayer Perceptron Network, a do uczenia sieci wykorzystano propagację wsteczną Levenberg-Marquardt. Krzywe S zostały obliczone za pomocą optymalizacji, a ich skuteczność w przewidywaniu zużycia węgla została oceniona statystycznie. Wyniki pokazują, że podejście oparte na optymalizacji opartej na krzywej S zapewnia większą dokładność niż SSN w rozwiązaniu przedstawionego problemu. Wyniki statystyczne obliczone przez GWO mają wyższą dokładność niż PSO, WOA i GA z R2 = 0,9881, RE = 0,011, RMSE = 1,079, MAE = 1,3584 i STD = 1,5187. Nowość tego badania, prezentowana metodyka nie wymaga dodatkowych parametrów wejściowych do analizy. Dzięki temu może być z łatwością wykorzystany z dużą dokładnością do oszacowania zużycia węgla w innych krajach o tendencji wzrostowej zużycia węgla, takich jak Turcja.
Źródło:
Gospodarka Surowcami Mineralnymi; 2022, 38, 2; 77--112
0860-0953
Pojawia się w:
Gospodarka Surowcami Mineralnymi
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks
Autorzy:
Kowalski, Piotr A.
Słoczyński, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/2055168.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
fuzzy neural network
fuzzy flip-flop neuron
particle swarm optimization
training procedure
sieć neuronowa rozmyta
optymalizacja rojem cząstek
procedura szkoleniowa
Opis:
The aim of the presented study is to investigate the application of an optimization algorithm based on swarm intelligence to the configuration of a fuzzy flip-flop neural network. Research on solving this problem consists of the following stages. The first one is to analyze the impact of the basic internal parameters of the neural network and the particle swarm optimization (PSO) algorithm. Subsequently, some modifications to the PSO algorithm are investigated. Approximations of trigonometric functions are then adopted as the main task to be performed by the neural network. As a result of the numerical verification of the problem, a set of rules are developed that can be helpful in constructing a fuzzy flip-flop type neural network. The obtained results of the computations significantly simplify the structure of the neural network in relation to similar conditions known from the literature.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 577--586
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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