Temat: Genetic-Particle Swarm Optimization

Skocz do pozycji: 1.

Tytuł:: Research on preventive maintenance strategy of Coating Machine based on dynamic failure rate
Autorzy:: Gu, Dongwei
Nie, Ruihua
Han, Wenbo
Chen, Guang
Jia, Ligang
Tematy:: dynamic preventive maintenance model
DM
BP-LSTM
dynamic failure rate threshold
preventive maintenance strategy
Genetic-Particle Swarm Optimization
GPSO; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Powiązania:: https://bibliotekanauki.pl/articles/24200819.pdf Link otwiera się w nowym oknie
Opis:: In this paper, a dynamic preventive maintenance strategy is proposed for the problem of high maintenance cost rate due to excessive maintenance caused by unreasonable maintenance threshold setting when complex electromechanical equipment maintenance strategy is formulated. Increasing failure rate factor and decreasing service age factor are introduced to describe the evolution rules of failure rate during the maintenance of the coating machine, and the BP-LSTM (BP-Long Short Term Memory Network, BP-LSTM) model is combined to predict the failure rate of the coating machine. A Dynamic preventive maintenance Model (DM) that relies on dynamic failure rate thresholds to classify the three preventive maintenance modes of minor, medium and major repairs is constructed. A dynamic preventive maintenance strategy optimization process based on Genetic-Particle Swarm Optimization (GPSO) algorithm with the lowest cost rate per unit time in service phase is built to solve the difficult problem of dynamic failure rate threshold finding. Based on the historical operating data of the coating machine, a case study of the dynamic preventive maintenance strategy of the coating machine was conducted to verify the effectiveness of the model and the developed maintenance strategy proposed in this paper. The results show that the maintenance strategy developed in this paper can ensure better economy and applicability.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A Comparative Study of PID Controller Tuning Using GA, EP, PSO and ACO
Autorzy:: Nagaraj, B.
Vijayakumar, P.
Tematy:: ant colony algorithm
evolutionary program
genetic algorithm particle swarm optimization and soft computing; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:: https://bibliotekanauki.pl/articles/384767.pdf Link otwiera się w nowym oknie
Opis:: Proportional - Integral - Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. How ever PID controller are poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This article comes up with a hybrid approach involving Genetic Algorithm (GA), Evolutionary Pro gramming (EP), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The proposed hybrid algorithm is used to tune the PID parameters and its per formance has been compared with the conventional me thods like Ziegler Nichols and Cohen Coon method. The results obtained reflect that use of heuristic algorithm based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. Speed control of DC motor process is used to assess the efficacy of the heuristic algorithm methodology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Comparative Study of Particle Swarm Optimization and Genetic Algorithms for Complex Mathematical Functions
Autorzy:: Valdez, F.
Melin, P.
Tematy:: genetic algorithms
particle swarm optimization (PSO)
hybrid systems
optimization; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:: https://bibliotekanauki.pl/articles/384575.pdf Link otwiera się w nowym oknie
Opis:: The Particle Swarm Optimization (PSO) and the Genetic Algorithms (GA) have been used successfully in solving problems of optimization with continuous and combinatorial search spaces. In this paper the results of the application of PSO and GAs for the optimization of mathematical functions are presented. These two methodologies have been implemented with the goal of making a comparison of their performance in solving complex optimization problems. This paper describes a comparison between a GA and PSO for the optimization of complex mathematical functions.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Function optimization using metaheuristics
Autorzy:: Pilski, M.
Seredyński, F.
Tematy:: particle swarm optimization (PSO)
artificial immune system
genetic algorithm
function optimization; Pokaż więcej
Wydawca:: Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Powiązania:: https://bibliotekanauki.pl/articles/92887.pdf Link otwiera się w nowym oknie
Opis:: The paper presents the results of comparison of three metaheuristics that currently exist in the problem of function optimization. The first algorithm is Particle Swarm Optimization (PSO) - the algorithm has recently emerged. The next one is based on a paradigm of Artificial Immune System (AIS). Both algorithms are compared with Genetic Algorithm (GA). The algorithms are applied to optimize a set of functions well known in the area of evolutionary computation. Experimental results show that it is difficult to unambiguously select one best algorithm which outperforms other tested metaheuristics.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Toward the best combination of optimization with fuzzy systems to obtain the best solution for the GA and PSO algorithms using parallel processing
Autorzy:: Valdez, Fevrier
Kawano, Yunkio
Melin, Patricia
Tematy:: genetic algorithms
particle swarm optimization (PSO)
fuzzy logic
parallel processing; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:: https://bibliotekanauki.pl/articles/384329.pdf Link otwiera się w nowym oknie
Opis:: In general, this paper focuses on finding the best configuration for PSO and GA, using the different migration blocks, as well as the different sets of the fuzzy systems rules. To achieve this goal, two optimization algorithms were configured in parallel to be able to integrate a migration block that allow us to generate diversity within the subpopulations used in each algorithm, which are: the particle swarm optimization (PSO) and the genetic algorithm (GA). Dynamic parameter adjustment was also performed with a fuzzy system for the parameters within the PSO algorithm, which are the following: cognitive, social and inertial weight parameter. In the GA case, only the crossover parameter was modified.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: A hybrid PSO-GA algorithm for Reversible Circuits Synthesis
Hybrydowy algorytm PSO-GA dla syntezy układów odwracalnych
Autorzy:: Podlaski, K.
Tematy:: reversible circuits
reversible logic synthesis
particle swarm optimization (PSO)
genetic algorithms
układy odwracalne
synteza układów odwracalnych
particle swarm optimization
algorytmy genetyczne; Pokaż więcej
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Powiązania:: https://bibliotekanauki.pl/articles/153468.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

Artykuł

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Skocz do pozycji: 7.

Tytuł:: A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm
Autorzy:: Dziwiński, Piotr
Bartczuk, Łukasz
Paszkowski, Józef
Tematy:: hybrid methods
Particle Swarm Optimization
Genetic Algorithm
fuzzy systems
multimodal function; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/1837533.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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na półce

Skocz do pozycji: 8.

Tytuł:: Optimization of electric and magnetic field intensities in proximity of power lines using genetic and particle swarm algorithms
Autorzy:: Król, K.
Machczyński, W.
Tematy:: power line
electric field
magnetic field
optimization
genetic algorithm
particle swarm algorithm; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/141588.pdf Link otwiera się w nowym oknie
Opis:: The paper presents optimization of power line geometrical parameters aimed to reduce the intensity of the electric field and magnetic field intensity under an overhead power line with the use of a genetic algorithm (AG) and particle swarm optimization (PSO). The variation of charge distribution along the conductors as well as the sag of the overhead line and induced currents in earth wires were taken into account. The conductor sag was approximated by a chain curve. The charge simulation method (CSM) and the method of images were used in the simulations of an electric field, while a magnetic field were calculated using the Biot–Savart law. Sample calculations in a three-dimensional system were made for a 220 kV single – circuit power line. A comparison of the used optimization algorithms was made.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Performance Comparison of Optimization Methods for Flat-Top Sector Beamforming in a Cellular Network
Autorzy:: Nandi, Pampa
Roy, Jibendu Sekhar
Tematy:: flat-top sector beam
particle swarm optimization
real-coded genetic algorithm; Pokaż więcej
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Powiązania:: https://bibliotekanauki.pl/articles/2142316.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

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
Tematy:: microcystis cell density
Multiple Linear Regression
Support Vector Machine
Particle Swarm Optimization
Genetic Algorithm
Bird Swarm Algorithm; Pokaż więcej
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Powiązania:: https://bibliotekanauki.pl/articles/2174666.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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