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Tytuł:
Cross‐Comparison of Evolutionary Algorithms for Optimizing Design of Sustainable Supply Chain Network under Disruption Risks
Autorzy:
Al-Zuheri, Atiya
Powiązania:
https://bibliotekanauki.pl/articles/2023790.pdf
Data publikacji:
2021
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
comparison
genetic algorithm
particle swarm optimization
sustainable supply chain design
disruption risk
porównanie
algorytm genetyczny
optymalizacja rojem cząstek
projektowanie zrównoważonego łańcucha dostaw
ryzyko zakłóceń
Opis:
Optimization of a sustainable supply chain network design (SSCND) is a complex decision-making process which can be done by the optimal determination of a set of decisions and constraints such as the selection of suppliers, transportation-related facilities and distribution centres. Different optimization techniques have been applied to handle various SSCND problems. Meta- heuristic algorithms are developed from these techniques that are commonly used to solving supply chain related problems. Among them, Genetic algorithms (GA) and particle swarm optimization (PSO) are implemented as optimization solvers to obtain supply network design decisions. This paper aims to compare the performance of these two evolutionary algorithms in optimizing such problems by minimizing the total cost that the system faces to potential disruption risks. The mechanism and implementation of these two evolutionary algorithms is presented in this paper. Also, using an optimization considers ordering, purchasing, inventory, transportation, and carbon tax cost, a numerical real-life case study is presented to demonstrate the validity of the effectiveness of these algorithms. A comparative study for the algorithms performance has been carried out based on the quality of the obtained solution and the results indicate that the GA performs better than PSO in finding lower-cost solution to the addressed SSCND problem. Despite a lot of research literature being done regarding these two algorithms in solving problems of SCND, few studies have compared the optimization performance between GA and PSO, especially the design of sustainable systems under risk disruptions.
Źródło:
Advances in Science and Technology. Research Journal; 2021, 15, 4; 342-351
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming
Autorzy:
Arif, Salah
Djellal, Adel
Djebbari, Nawel
Belhaoues, Saber
Touati, Hassen
Guellati, Fatma Zohra
Bensouilah, Mourad
Powiązania:
https://bibliotekanauki.pl/articles/2174666.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
microcystis cell density
Multiple Linear Regression
Support Vector Machine
Particle Swarm Optimization
Genetic Algorithm
Bird Swarm Algorithm
Opis:
Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.
Źródło:
Geomatics and Environmental Engineering; 2023, 17, 2; 31--68
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predicting and minimizing the blasting cost in limestone mines using a combination of gene expression programming and particle swarm optimization
Autorzy:
Bastami, Reza
Bazzazi, Abbas Aghajani
Shoormasti, Hadi Hamidian
Ahangari, Kaveh
Powiązania:
https://bibliotekanauki.pl/articles/1853861.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
kopalnia wapienia
wybuch detonacyjny
regresja nieliniowa
blasting cost
limestone mine
gene expression programming
non-linear multivariate regression
particle swarm optimization algorithm
environmental impacts
Opis:
Blasting cost prediction and optimization is of great importance and significance to achieve optimal fragmentation through controlling the adverse consequences of the blasting process. By gathering explosive data from six limestone mines in Iran, the present study aimed to develop a model to predict blasting cost, by gene expression programming method. The model presented a higher correlation coefficient (0.933) and a lower root mean square error (1088) comparing to the linear and nonlinear multivariate regression models. Based on the sensitivity analysis, spacing and ANFO value had the most and least impact on blasting cost, respectively. In addition to achieving blasting cost equation, the constraints such as frag-mentation, fly rock, and back break were considered and analyzed by the gene expression programming method for blasting cost optimization. The results showed that the ANFO value was 9634 kg, hole dia-meter 76 mm, hole number 398, hole length 8.8 m, burden 2.8 m, spacing 3.4 m, hardness 3 Mhos, and uniaxial compressive strength 530 kg/cm2 as the blast design parameters, and blasting cost was obtainedas 6072 Rials/ton, by taking into account all the constraints. Compared to the lowest blasting cost among the 146-research data (7157 Rials/ton), this cost led to a 15.2% reduction in the blasting cost and optimal control of the adverse consequences of the blasting process.
Źródło:
Archives of Mining Sciences; 2020, 65, 4; 835-850
0860-7001
Pojawia się w:
Archives of Mining Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dynamics of Stochastic vs. Greedy Heuristics in Traveling Salesman Problem
Autorzy:
Białogłowski, M.
Staniaszek, M.
Laskowski, W.
Grudniak, M.
Powiązania:
https://bibliotekanauki.pl/articles/91276.pdf
Data publikacji:
2018
Wydawca:
Warszawska Wyższa Szkoła Informatyki
Tematy:
traveling salesman problem
Nearest Neighbor
Monte Carlo
Simulated Annealing
Genetic Algorithm
particle swarm optimization (PSO)
Opis:
We studied the relative performance of stochastic heuristics in order to establish the relations between the fundamental elements of their mechanisms. The insights on their dynamics, abstracted from the implementation details, may contribute to the development of an efficient framework for design of new probabilistic methods. For that, we applied four general optimization heuristics with varying number of hyperparameters to traveling salesman problem. A problem-specific greedy approach (Nearest Neighbor) served as a reference for the results of: Monte Carlo, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization. The more robust heuristics – with higher configuration potential, i.e. with more hyperparameters – outperformed the smart ones, being surpassed only by the method specifically designed for the task.
Źródło:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2018, 12, 19; 7-24
1896-396X
2082-8349
Pojawia się w:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The influence of inertia weight on the Particle Swarm Optimization algorithm
Autorzy:
Cekus, D.
Skrobek, D.
Powiązania:
https://bibliotekanauki.pl/articles/122644.pdf
Data publikacji:
2018
Wydawca:
Politechnika Częstochowska. Wydawnictwo Politechniki Częstochowskiej
Tematy:
particle swarm optimization (PSO)
PSO algorithm
inertia weight
trajectory
optymalizacja rojem cząstek
PSO
algorytm PSO
metoda PSO
algorytm optymalizacji rojem cząstek
trajektoria
współczynnik wagowy
Opis:
The paper presents the use of the Particle Swarm Optimization (PSO) algorithm to find the shortest trajectory connecting two defined points while avoiding obstacles. The influence of the inertia weight and the number of population adopted in the first iteration of the PSO algorithm was examined for the length of the sought trajectory. Simulation results showed that the proposed method achieved significant improvement compared to the linearly decreasing method technique that is widely used in literature.
Źródło:
Journal of Applied Mathematics and Computational Mechanics; 2018, 17, 4; 5-11
2299-9965
Pojawia się w:
Journal of Applied Mathematics and Computational Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimization of Square-shaped Bolted Joints Based on Improved Particle Swarm Optimization Algorithm
Autorzy:
Chen, Kui
Yang, Cheng
Zhao, Yongsheng
Niu, Peng
Niu, NaNa
Hongchao, Wu
Powiązania:
https://bibliotekanauki.pl/articles/27312779.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
particle swarm optimization algorithm
bolt connection
bolted joint
fractal theory
Opis:
The bolted joint is widely used in heavy-duty CNC machine tools, which has huge influence on working precision and overall stiffness of CNC machine. The process parameters of group bolt assembly directly affect the stiffness of the connected parts. The dynamic model of bolted joints is established based on the fractal theory, and the overall stiffness of joint surface is calculated. In order to improve the total stiffness of bolted assembly, an improved particle swarm optimization algorithm with combination of time-varying weights and contraction factor is proposed. The input parameters are preloading of bolts, fractal dimension, roughness, and object thickness. The main goal is to maximize the global rigidity. The optimization results show that improved algorithm has better convergence, faster calculation speed, preferable results, and higher optimization performance than standard particle swarm optimization algorithm. Moreover, the global rigidity optimization is achieved.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 3; art. no. 168487
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimal design of RC frames using a modified hybrid PSOGSA algorithm
Optymalny projekt ramy RC z wykorzystaniem zmodyfikowanego algorytmu hybrydowego PSOGSA
Autorzy:
Chutani, S.
Singh, J.
Powiązania:
https://bibliotekanauki.pl/articles/230376.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
projekt optymalny
konstrukcja żelbetowa
optymalizacja demokratyczna roju cząstek
samoadaptacyjny algorytm
grawitacyjnego wyszukiwania
projektowanie
Indie
norma indyjska
optimum design
reinforced concrete structure
democratic particle swarm optimization
selfadaptive gravitational search algorithm
design
Indian standard
Opis:
The present study has been taken up to emphasize the role of the hybridization process for optimizing a given reinforced concrete (RC) frame. Although various primary techniques have been hybrid in the past with varying degree of success, the effect of hybridization of enhanced versions of standard optimization techniques has found little attention. The focus of the current study is to see if it is possible to maintain and carry the positive effects of enhanced versions of two different techniques while using their hybrid algorithms. For this purpose, enhanced versions of standard particle swarm optimization (PSO) and a standard gravitational search algorithm (GSA), were considered for optimizing an RC frame. The enhanced version of PSO involves its democratization by considering all good and bad experiences of the particles, whereas the enhanced version of the GSA is made self-adaptive by considering a specific range for certain parameters, like the gravitational constant and a set of agents with the best fitness values. The optimization process, being iterative in nature, has been coded in C++. The analysis and design procedure is based on the specifications of Indian codes. Two distinct advantages of enhanced versions of standard PSO and GSA, namely, better capability to escape from local optima and a faster convergence rate, have been tested for the hybrid algorithm. The entire formulation for optimal cost design of a frame includes the cost of beams and columns. The variables of each element of structural frame have been considered as continuous and rounded off appropriately to consider practical limitations. An example has also been considered to emphasize the validity of this optimum design procedure.
W niniejszym artykule przedstawiono bardziej realistyczny i optymalny projekt żlbetowych ram konstrukcyjnych (RC) poprzez hybrydyzację ulepszonych wersji standardowej optymalizacji roju cząsteczek (PSO) oraz standardowy algorytm wyszukiwania grawitacyjnego (GSA). Podejście proponowane w niniejszej pracy koncentruje się na hybrydyzacji ulepszonych wersji standardowej optymalizacji roju cząsteczek (PSO) oraz standardowym algorytmie wyszukiwania grawitacyjnego (GSA). PSO została zdemokratyzowana poprzez uwzględnienie wszystkich dobrych i złych doświadczeń w zakresie cząsteczek, podczas gdy GSA został zmieniony na samodostosowujący, uwzględniając określony zakres dla niektórych parametrów, takich jak np. stała grawitacyjna i zestaw czynników o najlepszych wartościach sprawności. Optymalny rozmiar i wzmocnienie elementów zostały określone dzięki zastosowaniu techniki w środowisku komputerowym, w którym cały proces analizy, projektowania i optymalizacji został zakodowany w C++. Procedura analizy i projektowania przebiega zgodnie ze specyfikacjami kodów indyjskich. Okazało się, że zastosowanie samodostosowującego algorytmu wyszukiwania grawitacyjnego wraz z demokratyczną techniką optymalizacji roju cząsteczek zapewnia dwie wyraźne przewagi nad standardową PSO i GSA, a mianowicie lepszą zdolność do ucieczki od lokalnej optymalności i szybszy współczynnik konwergencji. Całe sformułowanie dla optymalnego projektu kosztów ramy obejmuje zarówno koszt belek i słupów. W tym podejściu, zmienne każdego elementu ramy konstrukcyjnej zostały uznane za funkcje ciągłe i zaokrąglone odpowiednio do zastosowania praktycznego znaczenia niniejszego badania. Rozważono kilka przykładów, które podkreślają ważność optymalnej procedury projektowania, a wyniki porównano z wcześniejszymi badaniami, w celu sprawdzenia ich skuteczności i efektywności. Proponowany algorytm pokonuje ograniczenia dwóch indywidualnych algorytmów, biorąc pod uwagę ich hybrydę, a tym samym poprawia ogólną wydajność. Wprowadzono niezbędne zmiany, aby badanie było zgodne z wcześniejszymi badaniami. Porównanie z innymi wcześniej stosowanymi technikami hybrydowymi pokazuje, że czas potrzebny na przeprowadzenie procesu optymalizacji w niniejszym badaniu – z wykorzystaniem techniki MPSOGSA – został znacznie zmniejszony. Ponadto, podczas projektowania ram RC obniżono całkowity koszt za pomocą techniki MPSOGSA. Obniżenie kosztów w obszarze stali odgrywa większą rolę w optymalizacji, w porównaniu do redukcji kosztów w przekroju poprzecznym elementów ramy, co zostało szczegółowo przeanalizowane na przykładzie.
Źródło:
Archives of Civil Engineering; 2017, 63, 4; 123-134
1230-2945
Pojawia się w:
Archives of Civil Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Allocation of real power generation based on computing over all generation cost: an approach of Salp Swarm Algorithm
Autorzy:
Devarapalli, Ramesh
Sinha, Nikhil Kumar
Rao, Bathina Venkateswara
Knypiński, Łukasz
Lakshmi, Naraharisetti Jaya Naga
García Márquez, Fausto Pedro
Powiązania:
https://bibliotekanauki.pl/articles/1841291.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
economic load dispatch
heuristic algorithms
optimization
Particle Swarm
Algorithm
Salp Swarm Algorithm
ekonomiczna wysyłka ładunku
algorytmy heurystyczne
optymalizacja
rój cząstek
algorytm
Opis:
Economic Load Dispatch (ELD) is utilized in finding the optimal combination of the real power generation that minimizes total generation cost, yet satisfying all equality and inequality constraints. It plays a significant role in planning and operating power systems with several generating stations. For simplicity, the cost function of each generating unit has been approximated by a single quadratic function. ELD is a subproblem of unit commitment and a nonlinear optimization problem. Many soft computing optimization methods have been developed in the recent past to solve ELD problems. In this paper, the most recently developed population-based optimization called the Salp Swarm Algorithm (SSA) has been utilized to solve the ELD problem. The results for the ELD problem have been verified by applying it to a standard 6-generator system with and without due consideration of transmission losses. The finally obtained results using the SSA are compared to that with the Particle Swarm Optimization (PSO) algorithm. It has been observed that the obtained results using the SSA are quite encouraging.
Źródło:
Archives of Electrical Engineering; 2021, 70, 2; 337-349
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Design of a Superconducting Antenna Integrated with a Diplexer for Radio-Astronomy Applications
Autorzy:
Donelli, M.
Febvre, P.
Powiązania:
https://bibliotekanauki.pl/articles/309365.pdf
Data publikacji:
2014
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
diplexer
microwave antenna
optimization techniques
particle swarm algorithm
radio astronomy
Opis:
This paper presents the design of a compact frontend diplexer for radio-astronomy applications based on a self complementary Bow-tie antenna, a 3 dB T-junction splitter and two pass-band fractal lters. The whole diplexer structure has been optimized by using an evolutionary algorithm. In particular the problem of the diplexer design is recast into an optimization one by dening a suitable cost function which is then minimized by mean of an evolutionary algorithm namely the Particle Swarm Optimization (PSO). An X band diplexer prototype was fabricated and assessed demonstrating a good agreement between numerical and experimental results.
Źródło:
Journal of Telecommunications and Information Technology; 2014, 3; 113-118
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm
Autorzy:
Dziwiński, Piotr
Bartczuk, Łukasz
Paszkowski, Józef
Powiązania:
https://bibliotekanauki.pl/articles/1837533.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
hybrid methods
Particle Swarm Optimization
Genetic Algorithm
fuzzy systems
multimodal function
Opis:
The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 95-111
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On the hybridization of the artificial Bee Colony and Particle Swarm Optimization Algorithms
Autorzy:
El-Abd, M.
Powiązania:
https://bibliotekanauki.pl/articles/91658.pdf
Data publikacji:
2012
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
Artificial Bee Colony Algorithm
ABC
particle swarm optimization (PSO)
PSO
hybridization
hybrid algorithm
CEC05
Opis:
In this paper we investigate the hybridization of two swarm intelligence algorithms; namely, the Artificial Bee Colony Algorithm (ABC) and Particle Swarm Optimization (PSO). The hybridization technique is a component-based one, where the PSO algorithm is augmented with an ABC component to improve the personal bests of the particles. Three different versions of the hybrid algorithm are tested in this work by experimenting with different selection mechanisms for the ABC component. All the algorithms are applied to the well-known CEC05 benchmark functions and compared based on three different metrics, namely, the solution reached, the success rate, and the performance rate.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 2; 147-155
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Algorytmy stadne w problemach optymalizacji
Swarm Algorithms in Optimization Problems
Autorzy:
Filipowicz, B.
Kwiecień, J.
Powiązania:
https://bibliotekanauki.pl/articles/274567.pdf
Data publikacji:
2011
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
optymalizacja nieliniowa
algorytm PSO
algorytm pszczeli
algorytm świetlika
nonlinear optimization
particle swarm optimization (PSO)
bee algorithm
firefly algorithm
Opis:
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.
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.
Źródło:
Pomiary Automatyka Robotyka; 2011, 15, 12; 152-157
1427-9126
Pojawia się w:
Pomiary Automatyka Robotyka
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:
Economic dispatch in power system networks including renewable energy resources using various optimization techniques
Autorzy:
Hafiz, Abrar Mohamed
Abdelrahman, M. Ezzat
Temraz, Hesham
Powiązania:
https://bibliotekanauki.pl/articles/1841222.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Economic Dispatch (ED)
Particle Swarm Optimization (PSO)
Sine-Cosine
Algorithm (SCA)
Photovoltaic (PV)
Opis:
Economic dispatch (ED) is an essential part of any power system network. ED is how to schedule the real power outputs from the available generators to get the minimum cost while satisfying all constraints of the network. Moreover, it may be explained as allocating generation among the committed units with the most effective minimum way in accordance with all constraints of the system. There are many traditional methods for solving ED, e.g., Newton-Raphson method Lambda-Iterative technique, Gaussian-Seidel method, etc. All these traditional methods need the generators’ incremental fuel cost curves to be increasing linearly. But practically the input-output characteristics of a generator are highly non-linear. This causes a challenging non-convex optimization problem. Recent techniques like genetic algorithms, artificial intelligence, dynamic programming and particle swarm optimization solve nonconvex optimization problems in a powerful way and obtain a rapid and near global optimum solution. In addition, renewable energy resources as wind and solar are a promising option due to the environmental concerns as the fossil fuels reserves are being consumed and fuel price increases rapidly and emissions are getting higher. Therefore, the world tends to replace the old power stations into renewable ones or hybrid stations. In this paper, it is attempted to enhance the operation of electrical power system networks via economic dispatch. An ED problem is solved using various techniques, e.g., Particle Swarm Optimization (PSO) technique and Sine-Cosine Algorithm (SCA). Afterwards, the results are compared. Moreover, case studies are executed using a photovoltaic-based distributed generator with constant penetration level on the IEEE 14 bus system and results are observed. All the analyses are performed on MATLAB software.
Źródło:
Archives of Electrical Engineering; 2021, 70, 3; 643-655
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation
Autorzy:
He, Ping
Dong, Jie
Wu, Xiaopeng
Yun, Lei
Yang, Hua
Powiązania:
https://bibliotekanauki.pl/articles/27309934.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
BP neural network
photovoltaic power generation
PSO–GWO model
PSO–GWO–BP prediction model
particle swarm optimization
gray wolf optimization
back propagation
standard grey wolf algorithm
Opis:
At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
Źródło:
Archives of Electrical Engineering; 2023, 72, 3; 613--628
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 29

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