On the Parameter Determination of a Stress Relaxation Model Based on Creep Equations Using Differential Evolution Algorithm

2013 ◽  
Vol 441 ◽  
pp. 476-479
Author(s):  
Wei Wei Zhang ◽  
Hong Xu
Keyword(s):  
Stress Relaxation ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Parameter Determination ◽  
Model Parameters ◽  
Relaxation Model ◽  
Good Convergence ◽  
Model Based ◽  
De Algorithm ◽  
Creep And Stress Relaxation

A robust and efficient parameter identification method of the stress relaxation model based on Altenbach-Gorash-Naumenko creep equations is discussed. The differential evolution (DE) algorithm with a modified forward-Euler scheme is used in the identification procedure. Besides its good convergence properties and suitability for parallelization, initial guesses close to the solutions are not required for the DE algorithm. The parameter determination problem of the stress relaxation model is based on a very broad range specified for each parameter. The performance of the proposed DE algorithm is compared with a step-by-step model parameter determination technology and the genetic algorithm (GA). The model parameters of 12Cr-1Mo-1W-1/4V stainless steel bolting material at 550°C have been determined, and the creep and stress relaxation behaviors have been calculated. Results indicate that the optimum solutions can be obtained more easily by DE algorithm than others.

Parameter Identification of a Stress Relaxation Model Based on Differential Evolution Algorithm

2013 ◽  
Vol 842 ◽  
pp. 482-485
Author(s):  
Wei Wei Zhang ◽  
Hong Xu
Keyword(s):  
Stress Relaxation ◽  
Differential Evolution ◽  
Parameter Identification ◽  
Differential Evolution Algorithm ◽  
Parameter Determination ◽  
Model Parameters ◽  
Relaxation Model ◽  
Model Based ◽  
De Algorithm ◽  
Creep And Stress Relaxation

In order to determine the parameters of a stress relaxation model based on Altenbach-Gorash-Naumenko creep equations, an efficient parameter identification scheme is discussed. The differential evolution (DE) algorithm is used in the identification procedure with a modified forward-Euler scheme. The model parameters of 1Cr-0.5Mo-0.25V stainless steel bolting material at 500°C have been determined, and the creep and stress relaxation behaviors have been calculated. Comparing with a step-by-step model parameter determination technology and the genetic algorithm (GA), it shows that the DE algorithm has better convergence property and suitability for parallelization, and no need of initial guesses close to the solution. Results indicate that the optimum solutions can be obtained more easily by DE algorithm than others.

scholarly journals Global Sensitivity Analysis and Calibration by Differential Evolution Algorithm of HORTSYST Crop Model for Fertigation Management

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 610
Author(s):  
Antonio Martínez-Ruiz ◽  
Agustín Ruiz-García ◽  
J. Víctor Prado-Hernández ◽  
Irineo L. López-Cruz ◽  
J. Olaf Valencia-Islas ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Differential Evolution ◽  
Global Sensitivity Analysis ◽  
Differential Evolution Algorithm ◽  
Model Parameters ◽  
Growth Stages ◽  
Good Prediction ◽  
Area Index ◽  
Global Sensitivity ◽  
De Algorithm

Sensitivity analysis is the first step in elucidating how the uncertainties in model parameters affect the uncertainty in model outputs. Calibration of dynamic models is another issue of considerable interest, which is usually carried out by optimizing an objective function. The first aim of this research was to perform a global sensitivity analysis (GSA) with Sobol’s method for the 16 parameters of the new HORTSYST nonlinear model that simulates photo–thermal time (PTI), daily dry matter production DMP, nitrogen uptake (Nup), leaf area index (LAI), and crop transpiration (ETc). The second objective was to carry out the calibration of the HORTSYST model by applying a differential evolution (DE) algorithm as the global optimization method. Two tomato (Solanum lycopersicum L.) crops were established during the autumn–winter and spring–summer seasons under greenhouse and soilless culture conditions. Plants were distributed with a density of 3.5 plants m−2. Air temperature and relative humidity were measured with an S-THB-M008 model sensor. Global solar radiation was measured with an S-LIB-M003 sensor connected to a U-30-NRC datalogger. In the sensitivity analysis run in the two growth stages, it was observed that a greater number of parameters were more important at the beginning of fructification than at the end of crop growth for 10% and 20% of the variation of the parameters. The sensitivity analysis came up with nine parameters (RUE, a, b, c1 , c2, A, Bd, Bn, and PTIini) as the most important of the HORTSYST model, which were included in the calibration process with the DE algorithm. The best fit, according to RMSE, was for LAI, followed by Nup, DMP, and ETc for both crop seasons; the RMSE was close to zero, indicating a good prediction of the model’s performance.

scholarly journals Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110144
Author(s):  
Qianqian Zhang ◽  
Daqing Wang ◽  
Lifu Gao
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Inverse Kinematics ◽  
Differential Evolution Algorithm ◽  
Weight Coefficient ◽  
Adaptive Mutation ◽  
Feasible Region ◽  
Serial Manipulators ◽  
De Algorithm ◽  
Self Adaptive

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial manipulators, this article proposes a method for solving the IK of manipulators using an improved self-adaptive mutation differential evolution (DE) algorithm. First, based on the self-adaptive DE algorithm, a new adaptive mutation operator and adaptive scaling factor are proposed to change the control parameters and differential strategy of the DE algorithm. Then, an error-related weight coefficient of the objective function is proposed to balance the weight of the position error and orientation error in the objective function. Finally, the proposed method is verified by the benchmark function, the 6-DOF and 7-DOF serial manipulator model. Experimental results show that the improvement of the algorithm and improved objective function can significantly improve the accuracy of the IK. For the specified points and random points in the feasible region, the proportion of accuracy meeting the specified requirements is increased by 22.5% and 28.7%, respectively.

USING AN IMPROVED BEE MEMORY DIFFERENTIAL EVOLUTION ALGORITHM FOR PARAMETER ESTIMATION TO SIMULATE BIOCHEMICAL PATHWAYS

2014 ◽  
Vol 22 (01) ◽  
pp. 101-121 ◽  
Author(s):  
CHUII KHIM CHONG ◽  
MOHD SABERI MOHAMAD ◽  
SAFAAI DERIS ◽  
MOHD SHAHIR SHAMSIR ◽  
LIAN EN CHAI ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Estimation Algorithm ◽  
Convergence Time ◽  
Unknown Parameters ◽  
Estimation Algorithms ◽  
Bee Colony ◽  
De Algorithm ◽  
Cell Cycle Pathway ◽  
Parameter Values

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder–Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.

scholarly journals Optimization Of Raw Material Inventory Costs In The Food Supply Chain Using Differential Evolution Algorithm

2018 ◽  
Vol 73 ◽  
pp. 13016
Author(s):  
Mara Huriga Priymasiwi ◽  
Mustafid
Keyword(s):  
Supply Chain ◽  
Differential Evolution ◽  
Food Industry ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Raw Material ◽  
Inventory Cost ◽  
De Algorithm ◽  
Material Requirement ◽  
Total Inventory

The management of raw material inventory is used to overcome the problems occuring especially in the food industry to achieve effectiveness, timeliness, and high service levels which are contrary to the problem of effectiveness and cost efficiency. The inventory control system is built to achieve the optimization of raw material inventory cost in the supply chain in food industry. This research represents Differential Evolution (DE) algorithm as optimization method by minimizing total inventory based on amount of raw material requirement, purchasing cost, saefty stock and reorder time. With the population size, the parameters of mutation control, crossover parameters and the number of iterations respectively 80, 0.8, 0.5, 200. With the amount of safety stock at the company 7213.95 obtained a total inventory cost decrease of 39.95%. Result indicate that the use of DE algorithm help providein efficient amount, time and cost.

Differential Evolution Algorithm with Triangular Adaptive Control Parameter for SHEPWM Switching Pattern Optimization

2017 ◽  
Vol 8 (3) ◽  
pp. 1381
Author(s):  
Ismail Yusuf ◽  
Ayong Hiendro ◽  
F. Trias Pontia Wigyarianto ◽  
Kho Hie Khwee
Keyword(s):  
Adaptive Control ◽  
Differential Evolution ◽  
Control Parameter ◽  
Pulse Width Modulation ◽  
Differential Evolution Algorithm ◽  
De Algorithm ◽  
Harmonic Elimination ◽  
Switching Patterns ◽  
Convergence Process ◽  
Speed Up

Differential evolution (DE) algorithm has been applied as a powerful tool to find optimum switching angles for selective harmonic elimination pulse width modulation (SHEPWM) inverters. However, the DE’s performace is very dependent on its control parameters. Conventional DE generally uses either trial and error mechanism or tuning technique to determine appropriate values of the control paramaters. The disadvantage of this process is that it is very time comsuming. In this paper, an adaptive control parameter is proposed in order to speed up the DE algorithm in optimizing SHEPWM switching angles precisely. The proposed adaptive control parameter is proven to enhance the convergence process of the DE algorithm without requiring initial guesses. The results for both negative and positive modulation index (<em>M</em>) also indicate that the proposed adaptive DE is superior to the conventional DE in generating SHEPWM switching patterns

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