Wyniki wyszukiwania - Biblioteka Nauki

1

Modelling reference evapotranspiration by combining neuro fuzzy and evolutionary strategies

100%

Alizamir M. , Kisi O. , Muhammad Adnan R. , Kuriqi A.

Acta Geophysica

|

2020

|

tom Vol. 68, no. 4

1113--1126

EN

This study investigates the potential of two evolutionary neuro-fuzzy inference systems, adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (ANFIS–PSO) and genetic algorithm (ANFIS–GA), in modelling reference evapotranspiration (ET0). The hybrid models were tested using Nash–Sutclife efciency, root mean square errors and determination coefcient (R2 ) statistics and compared with classical ANFIS, artifcial neural networks (ANNs) and classifcation and regression tree (CART). Various combinations of monthly weather data of solar radiation, relative humidity, average air temperature and wind speed gotten from two stations, Antalya and Isparta, Turkey, were used as input parameters to the developed models to estimate ET0. The recommended evolutionary neuro-fuzzy models produced better estimates compared to ANFIS, ANN and CART in modelling monthly ET0. The ANFIS–PSO and/or ANFIS–GA improved the accuracy of ANFIS, ANN and CART by 40%, 32% and 66% for the Antalya and by 14%, 44% and 67% for the Isparta, respectively.

2

A tuning of PID power controller using particle swarm optimization for an electro-surgical unit

100%

Mohammed M. J. , Abed J. K. , Mahdi A. J. , Hussein B. M.

|

tom R. 99, nr 10

23--29

EN

Electro surgical unit is a popular modern device. It has been used in operating rooms for cutting, fulguration and coagulation of human tissues. ESU generates high frequency alternating current to prevent the stimulation of nerves and muscles. The objective of this article was to improve the performance of an ESU by controlling its output power under the variation of tissue impedance using proportional integral derivative controller based on particle swarm optimization to achieve minimum overshoots and fast dynamic response. The controller was simulated in MATLAB/SIMULINK to demonstrate the superiority of the suggested method. A comparative analysis was presented with ESU utilizing manual tuning process. The results showed that the proposed controller offered best performance utilizing manual tuning method. Moreover, both of the tuning methods presented better results from open-loop controller as a result of which charring of tissues could be eliminated and clinical operations could be made more efficient.

PL

Aparat elektrochirurgiczny to popularne nowoczesne urządzenie. Stosowano go na salach operacyjnych do cięcia, nakłuwania i koagulacji tkanek ludzkich. ESU generuje prąd zmienny o wysokiej częstotliwości, aby zapobiec stymulacji nerwów i mięśni. Celem tego artykułu była poprawa wydajności ESU poprzez sterowanie jego mocą wyjściową przy zmianie impedancji tkanki przy użyciu proporcjonalnego całkowego regulatora pochodnego opartego na optymalizacji roju cząstek w celu uzyskania minimalnych przeregulowań i szybkiej odpowiedzi dynamicznej. W celu wykazania wyższości zaproponowanej metody przeprowadzono symulację sterownika w programie MATLAB/SIMULINK. Przedstawiono analizę porównawczą z ESU z wykorzystaniem procesu strojenia ręcznego. Wyniki pokazały, że proponowany regulator oferował najlepszą wydajność przy zastosowaniu metody strojenia ręcznego. Co więcej, obie metody strojenia dały lepsze wyniki w przypadku sterowania z otwartą pętlą, w wyniku czego można było wyeliminować zwęglanie tkanek i zwiększyć efektywność operacji klinicznych.

3

A parallel improved PSO algorithm with genetic operators for 2D inversion of resistivity data

100%

Abril J. L. , Vasconcelos M. A. , Barboza F. M. , Mojica O. F.

Acta Geophysica

|

2022

|

tom Vol. 70, no 3

1137--1154

EN

In this paper, an improved particle swarm optimization technique known as elitist-mutated particle swarm optimization (EMPSO) was applied in the 2D electrical resistivity imaging, a complex and highly nonlinear optimization problem. The EMPSO enables better exploration of the search space, by replacing particles with a worse performance by the best particle of the swarm mutated in random positions. Nevertheless, this technique, as any other based on a population of models, costs much computation time in solving optimization problems with a large number of unknown parameters. We addressed this problem by developing a parallel version of the EMPSO that supports pure MPI and hybrid MPI-OpenMP modes, and we named as parallel elitist-mutated PSO (PEMPSO). The solution to the inverse problem is based on minimizing an objective function with a regularization term to create a mathematically stable solution. Total variation and global smoothness regularizations were used in the inversion of synthetic data obtained from simple models and a set of real data of a highly complex geological/geotechnical nature. By virtue of the features of the synthetic models and the geology of the local where the data were acquired, the inversions with total variation regularization provided the best outcomes. Additionally, we have improved the execution time significantly with our parallel solution (the pure MPI model turned out to be better than the hybrid model) in comparison with the sequential version. Cumulative frequency distribution of errors between modeled and observed apparent resistivity data for all experiments was used to validate the PEMPSO technique for estimating resistivity.

4

Optimal power system stabilizer design using craziness particle swarm optimization in Sulselrabar system

84%

Djalal M. R. , Saini M. , Yunus A. M. S. , Kitta I.

Przegląd Elektrotechniczny

|

2021

|

tom R. 97, nr 10

76--81

EN

Power System Stabilizer (PSS) is a supplementary control that provides additional control actions on the excitation side of the generator. In this study a Craziness Particle Swarm Optimization (CRPSO) based tuning method is proposed to optimize the PSS parameters. CRPSO is a development of the conventional PSO method, where in conventional PSO there is a tendency to achieve premature convergence. This condition causes the solution obtained to be the optimum local. With optimal PSS parameters, the optimal PSS performance is obtained. The combination of PSS and excitation is used to reduce the oscillation that occurs in the system. In this research a case study of load addition and load shedding is used. From the simulation results, it is found that system performance is more optimal using CRPSO than using conventional PSO. System performance is shown by the response of the generator speed and rotor angle which results in a small overshoot and a faster settling time when there is an increase in load and also load shedding. Increased system performance is also viewed from the negative system eigenvalue, negative eigenvalue indicates the system is stable.

PL

Stabilizator systemu zasilania (PSS) jest dodatkowym sterowaniem, które zapewnia dodatkowe działania sterujące po stronie wzbudzenia generatora. W tym badaniu zaproponowano metodę strojenia opartą na Craziness Particle Swarm Optimization (CRPSO) w celu optymalizacji parametrów PSS. CRPSO jest rozwinięciem tradycyjnej metody PSO, gdzie w konwencjonalnym PSO istnieje tendencja do osiągnięcia przedwczesnej konwergencji. Stan ten powoduje, że otrzymane rozwiązanie jest optymalne miejscowo. Przy optymalnych parametrach PSS uzyskuje się optymalną wydajność PSS. Połączenie PSS i wzbudzenia służy do zmniejszenia oscylacji występujących w systemie. W tym badaniu wykorzystano studium przypadku dodawania i odciążania. Z symulacji wynika, że wydajność systemu jest bardziej optymalna przy użyciu CRPSO niż przy użyciu konwencjonalnego PSO. Wydajność systemu jest pokazana przez reakcję prędkości generatora i kąta wirnika, co skutkuje niewielkim przeregulowaniem i szybszym czasem ustalania, gdy występuje wzrost obciążenia, a także zmniejszenie obciążenia. Zwiększona wydajność systemu jest również postrzegana z ujemnej wartości własnej systemu, ujemna wartość własna wskazuje, że system jest stabilny.

5

Developing numerical equality to regional intensity-duration-frequency curves using evolutionary algorithms and multi-gene genetic programming

84%

Citakoglu H. , Demir V.

|

2023

|

tom Vol. 71, no. 1

469--488

EN

This study aims to carry out regional intensity−duration−frequency (IDF) equality using the relationship with IDF obtained from point frequency analysis. Eleven empirical equations used in the literature for seven climate regions of Turkey were calibrated using particle swarm optimization (PSO) and genetic algorithm (GA) optimization techniques and the obtained results were compared. In addition, in this study, new regional IDF equations were obtained for each region utilizing Multi-Gene Genetic Programming (MGGP) method. Finally, Kruskal–Wallis (KW) test was applied to the IDF values obtained from the methods and the observed values. As a result of the study, it was observed that the coefficients of 11 empirical equations calibrated with PSO, and GA techniques were different from each other. The mean absolute error (MAE), root mean square error (RMSE), mean absolute relative error (MARE), coefficient of determination (R2 ), and Taylor diagram were used to evaluate the performances of PSO, GA, and MGGP techniques. According to the performance criteria, it has been determined that the IDF equations obtained by the MGGP method for the Eastern Anatolia, Aegean, Southeastern Anatolia, and Central Anatolia regions are more successful than the empirical equations calibrated with the PSO and GA method. The empirical IDF equations produced with PSO and the IDF equations acquired with MGGP have similar findings in the Mediterranean, Black Sea, and Marmara. In addition, the KW test results showed that the data of all models were from the same population.

6

Optimal Intelligent Control for HVAC Systems

84%

Khooban M. H. , Soltanpour M. R. , Abadie D. N. M. , Esfahani Z.

Journal of Power Technologies

|

2012

|

tom Vol. 92, nr 3

192--200

EN

In this paper a novel Optimal Fuzzy Proportional-Integral-Derivative Controller (OFPIDC) is designed for controlling the air supply pressure of Heating, Ventilation and Air-Conditioning (HVAC) system. The parameters of input membership functions, output polynomial functions of first-order Sugeno, and PID controller coefficients are optimized simultaneously by random inertia weight Particle Swarm Optimization (RNW-PSO). Simulation results show the superiority of the proposed controller than similar non-optimal fuzzy controller.

7

Efficiency Improvement of Axial Flux PM Motor Using Particle Swarm Optimisation

84%

Cvetkovski G. , Petkovska L.

Przegląd Elektrotechniczny

|

2013

|

tom R. 89, nr 2b

38--41

EN

In this paper a particle swarm based optimal design of axial field permanent magnet motor (AFPMM) is proposed. This approach employs a particle-swarm-optimization (PSO) technique to search for optimal design solution of an AFPMM based on the efficiency value of the motor. A comparative analysis of the optimised solution and the prototype is presented and it is based on the values of the optimised objective function, on the values of the optimisation parameters, and on a set of electric and magnetic parameters of the motor.

PL

W artykule opisano wykorzystanie metody optymalizacji roju cząstek w projektowaniu maszyny synchronicznej z magnesami trwałymi o strumieniu osiowym (ang. AFPMM). Dokonano analizy porównawczej wyznaczonych optymalizacji oraz przedstawiono prototyp oparty na wartościach parametrów optymalizacji oraz parametrach elektrycznych i magnetycznych maszyny.

8

Structural design optimization of steel beamsand frames with web-tapered members usingthe PSO-FEM algorithm

84%

Sych P. , Słoński M.

|

tom Vol. 28, no. 1

39--55

EN

This paper presents an algorithm for structural design optimization of steel beams andframes with web-tapered members using the particle swarm optimization (PSO) algorithmand the finite element method (FEM). The design optimization is done in accordancewith Eurocode 3 (EC 3) for the minimum mass. The proposed algorithm is more flexibleand efficient than traditional design methods based on a trial and error approach. Theeffectiveness of the presented PSO-FEM algorithm is evaluated on examples of the sizeoptimization of web-tapered members cross-section. The results show that the PSO-FEM algorithm is feasible and effective for finding useful designs.

9

Efficient Evolutionary Particle Swarm Optimization Approach for Nonconvex Economic Load Dispatch Problem

84%

Abdullah M. N. , Rahim N. A. , Bakar A. H. A. , Mokhlis H. , Illias H. A. , Jamian J. J.

Przegląd Elektrotechniczny

|

2013

|

tom R. 89, nr 2a

139--143

EN

The main objective of economic load dispatch (ELD) is to allocate the output power generator at minimum cost while satisfying all the operation constraints. This paper presents a new hybrid method by integrating particle swarm optimization with time varying acceleration coefficients and evolutionary programming (TVAC-EPSO) for solving nonconvex ELD problem. The competition, sorting and selection in EP method are used to determine the best particle in PSO for finding the optimum solution efficiently. The proposed TVAC-EPSO has been tested on three different power system benchmarks. The simulation results have demonstrated the effectiveness of the proposed method in solving nonconvex ELD problem.

PL

W artykule przedstawiono hybrydową metodę ekonomicznie uzasadnionego określenia założeń dotyczących generowanej energii elektrycznej (ang. Economic Load Dispatch - ELD). Algorytm oparty jest na wykorzystaniu metody optymalizacji roju cząstek ze współczynnikami zmiennymi w czasie i programowaniu ewolucyjnym. (ang. TVAC-EPSO). Proponowana metoda została poddana weryfikacji na trzech różnych systemach energetycznych. Wyniki symulacyjne potwierdzają jej efektywność w analizie problemu ELD.

10

An Effective Integrated Metaheuristic Algorithm For Solving Engineering Problems

84%

Alihodzic A. , Delalic S. , Gusic D.

Annals of Computer Science and Information Systems

|

2020

|

tom Vol. 21

207--214

EN

To tackle a specific class of engineering problems, in this paper, we propose an effectively integrated bat algorithm with simulated annealing for solving constrained optimization problems. Our proposed method (I-BASA) involves simulated annealing, Gaussian distribution, and a new mutation operator into the simple Bat algorithm to accelerate the search performance as well as to additionally improve the diversification of the whole space. The proposed method performs balancing between the grave exploitation of the Bat algorithm and global exploration of the Simulated annealing. The standard engineering benchmark problems from the literature were considered in the competition between our integrated method and the latest swarm intelligence algorithms in the area of design optimization. The simulations results show that I-BASA produces high-quality solutions as well as a low number of function evaluations.

11

Optimal configuration of standalone wind-solar-storage complementary generation system based on the GA-PSO algorithm

84%

Sun Q. , Ma J. , She Y. , Zhang J. , Gu B. , Zhang Z.

Journal of Power Technologies

|

2019

|

tom Vol. 99, nr 4

231--236

EN

The capacity configuration of the standalone wind–solar–storage complementary power generation system (SWS system) is affected by environmental, climate condition, load and other stochastic factors. This makes the capacity configuration of the SWS system problematic when the capacity configuration method of traditional power generation is used. An optimal configuration method of the SWS system based on the hybrid genetic algorithm and particle swarm optimization (GA-PSO) algorithm is proposed in this study to improve the stability and economy of the SWS system. The constituent elements of investment, maintenance cost and various reliability constraints of the SWS system were also discussed. The optimal configuration of the SWS system based on GA-PSO was explored to achieve the optimization objective, which was to minimize investment and maintenance costs of the SWS system while maintaining power supply reliability. The investment and maintenance costs of the SWS system under different configuration methods were calculated and analyzed on the bases of the monthly mean wind speed, solar radiation and load data of Xiaoertai Village in Zhangbei County of Hebei Province in the last 10 years. Results show that the optimal configuration method based on the GA-PSO algorithm could effectively improve the economy of the system and meet the requirements of system stability. The proposed method shows great potential for guiding the optimal configuration of the SWS system in remote areas.

12

Development of hybrid performance‑based optimization algorithm for structures equipped with vibration damper devices

84%

Ayyash A. j. N. A. , Hejazi F.

Archives of Civil and Mechanical Engineering

|

2023

|

tom Vol. 23, no. 2

art. no. e123, 2023

EN

Nowadays, various types of vibration damping systems are being implemented in different buildings to diminish seismic effects on structures. However, engineers are faced with the challenging task of developing an optimum design for structures utilizing a proper type of damping device based on new techniques such as the performance-based design method. Therefore, this research was aimed at developing a multi-objective optimization algorithm by hybridizing the particle swarm optimization (PSO) and gravitational search algorithm (GSA) to obtain an optimum design for structures equipped with vibration damper devices based on the performance-based design method. Then, the developed hybrid algorithm (PSOGSA) would be capable of optimizing the damping system simultaneously with the optimized details of the structural sections, including the steel rebars, by satisfying all the design criteria. For this purpose, a special process for the design of structures equipped with vibration damper devices according to the performance-based design method was developed by considering of a wide range of vibration damping systems. The proposed PSOGSA optimization framework was then implemented to design a 12-storey reinforced concrete structure equipped with different types of dampers to minimize the structural weight while satisfying all the prescribed performance-based design acceptance criteria. The results indicated that the proposed optimization method was able to successfully optimize the details of the structural members as well as the type and properties of the damper, which significantly improved the structural response in terms of the formation of plastic hinges and the structural movements.

13

Complex lithology prediction using mean impact value, particle swarm optimization, and probabilistic neural network techniques

84%

Gu Y. , Zhang Z. , Zhang D. , Zhu Y. , Bao Z. , Zhang D.

Acta Geophysica

|

2020

|

tom Vol. 68, no. 6

1727--1752

EN

Lithology prediction is a fundamental problem because the outcome of lithology prediction is the critical underlying data for some basic geological work, e.g., establishing stratigraphic framework or analyzing distribution of sedimentary facies. As the geological formation generally consists of many diferent lithologies, the lithology prediction is always viewed as a tough work by geologists. Probabilistic neural network (PNN) shows high efciency when solving pattern recognition problem since learning data do not need to do any pre-training of learning data and calculation results are universally reliable, and then, this model could be considered as an efective solution. However, there are two factors that seriously limit the PNN’s performance: One is existence of the interference variables of learning samples, and the other is selection of the window length of probability density distribution. In view of adverse impact of those two factors, two techniques, mean impact value (MIV) and particle swarm optimization (PSO), are introduced to improve the PNN’s calculation capability. Thus, a new prediction method referred as MIV–PSO–PNN is proposed in this paper. The proposed method is validated by three well-designed experiments, and the corresponding experiment data are recorded by two cored wells of the LULA oilfeld. For the three experiments, prediction accuracies of the results provided by the proposed method are 81.67%, 73.34% and 88.34%, respectively, all of which are higher than those provided by other comparative approaches including backpropagation (BP), PNN, and MIV-PNN. The experiment results strongly demonstrate that the proposed method is capable to predict complex lithology.

14

Coordinated control strategy for microgrid stability maintenance under isolated island operation

84%

Wu P. , Xu X.

Archives of Electrical Engineering

|

2021

|

tom Vol. 70, nr 2

285--295

EN

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.

15

The application of different optimization techniques and Artificial Neural Networks (ANN) for coal-consumption forecasting: a case study

84%

Seker M. , Unal Kartal N. , Karadirek S. , Gulludag C. B.

Gospodarka Surowcami Mineralnymi

|

2022

|

tom T. 38, z. 2

77--112

EN

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.

PL

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.

16

Optimum placement of multi type DG units for loss reduction in a radial distribution system considering the distributed generation

84%

Shanmugapriyan J. , Karuppiah N. , Muthubalaji S. , Tamilselvi S.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2018

|

tom Vol. 66, nr 3

345--354

EN

Due to the increasing need for electricity, insertion of distributed generation (DG) into a distribution system attracts the attention of the deregulated power market. Placing DG in the distribution system inherently reduces the power loss and improves the system voltage profile. The choice of DG, proper placement and sizing of DG all play a vital role. This paper presents an effective methodology to identify the optimum location of multi type DG in the distribution system. The particle swarm optimization (PSO) algorithm and differential evolution (DE) are applied to identify the proper location and size of DG using the distributed generation suitability index (DGSI). The optimum location of DG is identified through DGSI and optimum sizing is done by means of the power loss minimization technique using evolutionary algorithms. The effective power loss reduction and improved system voltage profile are evaluated using sixteen combinations of different types of DGs with the standard IEEE 33-bus test system. The results reveal that power loss reduction and voltage profile improvement are effectively addressed by the DE algorithm.

17

A hybrid cooperative quantum particle swarm optimizer with dynamic varying search area for function optimization

84%

Li D.

Przegląd Elektrotechniczny

|

2013

|

tom R. 89, nr 3b

21--24

EN

This paper proposes a hybrid cooperative quantum particle swarm optimization (HCQPSO), hybridizing dynamic varying search area, cooperative evolution, simulated annealing and quantum particle swarm optimization (PSO) for function optimization. In the proposed HQCPSO, a technique of dynamic varying search area helps reduce the search spaces and populations of swarms, which could make the optimization more efficient. Simulated annealing is integrated in the position update to modify the trajectories of particles to avoid being trapped in the local optimum. To test the performance of HQCPSO, numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on benchmark test functions.

PL

W artykule zaproponowano hybrydowy algorytm optymalizacji PSO. Porównanie z innymi, znanymi wariantami wykazało, że zastosowane w metodzie rozwiązania, pozwalają na efektywniejsze działanie proponowanego algorytmu PSO. Wyniki eksperymentalne potwierdziły powyższą tezę.

18

Prediction of flyrock distance induced by blasting using particle swarm optimization and multiple regression analysis: an engineering perspective

84%

Chen Y. , Wang M. , Yin H. , Zhang T.

Acta Geophysica

|

2024

|

tom Vol. 72, no. 1

287--301

EN

Flyrock is one of the major safety hazards induced by blasting operations. However, few studies were for predicting blasting-induced flyrock distance from the perspective of engineers. The present paper attempts to provide an engineer-friendly equation predicting blasting-induced flyrock distance. Data used in the present study contains s seven blasting parameters including borehole diameter, blasthole length, powder factor, stemming length, maximum charge per delay, burden, and flyrock distance is obtained. Data is inputted into Random Forest for feature selection. The selected features are formulated as two candidate equations, including Multiple Linear Regression (MLR) equation and Multiple Nonlinear Regression (MNR) equation. Those two candidates are respectively referred by Particle Swarm Optimization for searching optimum values for the coefficients of selected features. It is proved that MLR equation has better accuracy. MLR equation is compared with two empirical equations and the MLR equation based on least squares method. It is found that the coefficient of correlation of the proposed MLR equation reaches 0.918, which is the highest compared with the scores of other three equations. The present study utilizes feature selection process to screen inputs, which effectively excludes irrelevant parameters from being considered. Plus the contribution of Particle Swarm Optimization, the accuracy of the obtained equation can be guaranteed.

19

Particle swarm optimization of a neural network model for predicting the flashover voltage on polluted cap and pin insulator

84%

Belkebir A. , Bourek Y. , Benguesmia H.

Diagnostyka

|

2022

|

tom Vol. 23, No. 3

art. no 2022309

EN

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.

20

Multi-channeled MR brain image segmentation: A novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation

84%

Narayanan A. , Rajasekaran M. P. , Zhang Y. , Govindaraj V. , Thiyagarajan A.

Biocybernetics and Biomedical Engineering

|

2019

|

tom Vol. 39, no. 2

350--381

EN

Growth of cancer cells within the human body is a major outcome of the manipulation of cells and it has resulted in the deterioration of the life span of humans. The impact of cancer cells is irretrievable and it has paved the way to the formation of tumors within the human body. For achieving and developing a single-structured framework to prominently identify the tumor regions and segmenting the tissue structures specifically in human brain, a novel combinational algorithm is proposed through this paper. The algorithm has been embodied with two optimization techniques namely particle swarm optimization (PSO) and bacteria foraging optimization (BFO), wherein, PSO helps in finding the best position of global bacterium for BFO, consecutively, BFO supports the modified fuzzy c means (MFCM) algorithm by providing optimized cluster heads. Finally, MFCM segments the tissue regions and identifies the tumor portion, thereby reducing the interaction and complication experienced by a radiologist during patient diagnosis. The strength of the proposed algorithm is proven by comparing it with the state-of-the-art techniques by means of evaluation parameters like mean squared error (MSE), peak signal to noise ratio (PSNR), sensitivity, specificity, etc., Data sets used in this paper were exclusively obtained from hospital, Brain web simulator and BRATS-2013 challenge. The sensitivity and specificity values for 115 MR brain slice images.