scholarly journals Research on the Grey Verhulst Model Based on Particle Swarm Optimization and Markov Chain to Predict the Settlement of High Fill Subgrade in Xiangli Expressway

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Haiming Liu ◽  
Wei Guo ◽  
Chao Zhang ◽  
Huaihao Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Chain ◽  
Particle Swarm ◽  
Time Interval ◽  
Grey Theory ◽  
Swarm Optimization ◽  
Model Based ◽  
Grey Verhulst Model ◽  
Competing Models ◽  
Verhulst Model

It is of vital significance to accurately forecast the settlement of high fill subgrade, which is the foundation for disaster prevention and treatment of subgrade. According to the monitoring data of high fill subgrade, a novel model, called PSOMGVM model, based on particle swarm optimization (PSO) and Markov chain is proposed. Firstly, the typical characteristics of settlement curve are analyzed from the aspect of geomechanics theory and based on the grey theory, the grey Verhulst model (GVM) with unequal time-interval is proposed. Then, according to the theory of Markov chain, the grey Verhulst model is built to revise the relative residuals of the GVM, in which the effects of volatility characteristics can be considered. Finally, the PSOMGVM model based on PSO algorithm and Markov chain is set up, which whitens the parameters of the grey interval. In order to demonstrate the fitness and the ability of the proposed model, five competing models are introduced to predict the settlement of the high fill subgrade of Xiangli Expressway in Yunnan Province. Through the analysis of APE, MAPE, and RMSE, it states that the accuracy and performance of the PSOMGVM model outperform the other five competing models for simulative and predictive periods.

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

scholarly journals Exact Markov chain-based runtime analysis of a discrete particle swarm optimization algorithm on sorting and OneMax

2021 ◽  
Author(s):  
Moritz Mühlenthaler ◽  
Alexander Raß ◽  
Manuel Schmitt ◽  
Rolf Wanka
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Chain ◽  
Markov Model ◽  
Optimization Problems ◽  
Formal Analysis ◽  
Particle Swarm ◽  
Upper And Lower Bounds ◽  
Swarm Optimization ◽  
Local Optima ◽  
Sorting Problem

AbstractMeta-heuristics are powerful tools for solving optimization problems whose structural properties are unknown or cannot be exploited algorithmically. We propose such a meta-heuristic for a large class of optimization problems over discrete domains based on the particle swarm optimization (PSO) paradigm. We provide a comprehensive formal analysis of the performance of this algorithm on certain “easy” reference problems in a black-box setting, namely the sorting problem and the problem OneMax. In our analysis we use a Markov model of the proposed algorithm to obtain upper and lower bounds on its expected optimization time. Our bounds are essentially tight with respect to the Markov model. We show that for a suitable choice of algorithm parameters the expected optimization time is comparable to that of known algorithms and, furthermore, for other parameter regimes, the algorithm behaves less greedy and more explorative, which can be desirable in practice in order to escape local optima. Our analysis provides a precise insight on the tradeoff between optimization time and exploration. To obtain our results we introduce the notion of indistinguishability of states of a Markov chain and provide bounds on the solution of a recurrence equation with non-constant coefficients by integration.

Grey Model Based Particle Swarm Optimization Algorithm for Anticorrosion Reliability Design of Underground Pipelines

2010 ◽  
Vol 118-120 ◽  
pp. 541-545
Author(s):  
Qin Ming Liu ◽  
Ming Dong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Failure Modes ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Grey Model ◽  
Swarm Optimization ◽  
Reliability Design ◽  
Model Based ◽  
Underground Pipelines

This paper explores the grey model based PSO (particle swarm optimization) algorithm for anti-cauterization reliability design of underground pipelines. First, depending on underground pipelines’ corrosion status, failure modes such as leakage and breakage are studied. Then, a grey GM(1,1) model based PSO algorithm is employed to the reliability design of the pipelines. One important advantage of the proposed algorithm is that only fewer data is used for reliability design. Finally, applications are used to illustrate the effectiveness and efficiency of the proposed approach.

scholarly journals Forecasting Crude Oil Consumption in China Using a Grey Prediction Model with an Optimal Fractional-Order Accumulating Operator

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Huiming Duan ◽  
Guang Rong Lei ◽  
Kailiang Shao
Keyword(s):  
Particle Swarm Optimization ◽  
Crude Oil ◽  
Fractional Order ◽  
Gamma Function ◽  
Particle Swarm ◽  
Grey Theory ◽  
Grey Prediction ◽  
Oil Consumption ◽  
Swarm Optimization ◽  
Production And Consumption

Crude oil, which is an important part of energy consumption, can drive or hinder economic development based on its production and consumption. Reasonable predictions of crude oil consumption in China are meaningful. In this paper, we study the grey-extended SIGM model, which is directly estimated with differential equations. This model has high simulation and prediction accuracies and is one of the important models in grey theory. However, to achieve the desired modeling effect, the raw data must conform to a class ratio check. Unfortunately, the characteristics of the Chinese crude oil consumption data are not suitable for SIGM modeling. Therefore, in this paper, we use a least squares estimation to study the parametric operation properties of the SIGM model, and the gamma function is used to extend the integer order accumulation sequence to the fractional-order accumulation generation sequence. The first-order SIGM model is extended to the fractional-order FSIGM model. According to the particle swarm optimization (PSO) mechanism and the properties of the gamma function of the fractional-order cumulative generation operator, the optimal fractional-order particle swarm optimization algorithm of the FSIGM model is obtained. Finally, the data concerning China’s crude oil consumption from 2002 to 2014 are used as experimental data. The results are better than those of the classical grey GM, DGM, and NDGM models as well as those of the grey-extended SIGM model. At the same time, according to the FSIGM model, this paper predicts China’s crude oil consumption for 2015–2020.

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