Gbest guided differential evolution

Mokan, Monika; Sharma, Kapil; Sharma, Harish; Verma, Chakradhar

doi:10.1109/iciinfs.2014.7036663

Cited by 13 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first selected method was DE [14,[17][18][19][20][21][22][23][24][25][26]. Some of DE's advantages are ease of use, simple structure, speed and robustness.…”

Section: Used Evolutionary Methods and Algorithmmentioning

confidence: 99%

“…The Objective Function (OF) is defined as the sum of squares of differences between the speed and current responses. It is written in (17).…”

Section: Objective Functionmentioning

confidence: 99%

“…Different ones have been tested, with the aim of determining a fast and robust method. The first one was Differential Evolution (DE) [14,[17][18][19][20][21][22][23][24][25][26], and three different strategies were tested. Also tested were the state-ofthe-art Artificial Bee Colony (ABC) [27][28][29][30][31][32][33][34] and Teaching Learning Based Optimization (TLBO) [35][36][37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Innovative Approach for the Determination of a DC Motor’s and Drive’s Parameters Using Evolutionary Methods and Different Measured Current and Angular Speed Responses

Jesenik,

Ravber,

Trbušić

2023

Mathematics

View full text Add to dashboard Cite

The determination is presented of seven parameters of a DC motor’s drive. The determination was based on a comparison between the measured and simulated current and speed responses. For the parameters’ determination, different evolutionary methods were used and compared to each other. The mathematical model presenting the DC drives model was written using two coupled differential equations, which were solved using the Runge–Kutta first-, second-, third- and fourth-order methods. The approach allows determining the parameters of controlled drives in such a way that the controller is taken into account with the measured voltage. Between the tested evolutionary methods, which were Differential Evolution with three strategies, Teaching-Learning Based Optimization and Artificial Bee Colony, the Differential Evolution (DE/rand/1/exp) can be suggested as the most appropriate for the presented problem. Measurements with different sampling times were used, and it was found out that at least some measuring points should be at the speed-up interval. Different lengths of the measured signal were tested, and it is sufficient to use a signal consisting of the drive’s acceleration and a short part of the stationary operation. The analysis showed that the procedure has good repeatability. The biggest deviation of calculated parameters considering 10 repeated measurements was 6% in case of the La calculation. The deviations of all the other parameters’ calculations were less than 2%.

show abstract

“…The first selected method was DE [14,[17][18][19][20][21][22][23][24][25][26]. Some of DE's advantages are ease of use, simple structure, speed and robustness.…”

Section: Used Evolutionary Methods and Algorithmmentioning

confidence: 99%

“…The Objective Function (OF) is defined as the sum of squares of differences between the speed and current responses. It is written in (17).…”

Section: Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Innovative Approach for the Determination of a DC Motor’s and Drive’s Parameters Using Evolutionary Methods and Different Measured Current and Angular Speed Responses

Jesenik,

Ravber,

Trbušić

2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…The improper determination of model parameters due to the improper selection of the optimization method can lead to an incorrect evaluation of model quality. The used methods were genetic algorithm (GA) [21][22][23][24][25][26], differential evolution (DE) with three different strategies, which are DE/rand/1/exp, DE/rand/2/exp, DE/best/1/bin [27][28][29][30][31][32][33][34][35][36], teaching-learning-based optimization (TLBO) [37][38][39][40][41][42][43][44][45] and artificial bee colony (ABC) [46][47][48][49][50][51][52][53][54][55]. If an inappropriate solving method is used, the adequacy of the mathematical expression will not be determined correctly.…”

Section: Introductionmentioning

confidence: 99%

Determination of a Hysteresis Model Parameters with the Use of Different Evolutionary Methods for an Innovative Hysteresis Model

2020

View full text Add to dashboard Cite

For precise modeling of electromagnetic devices, we have to model material hysteresis. A Genetic Algorithm, Differential Evolution with three different strategies, teaching–learning-based optimization and Artificial Bee Colony, were used for testing seven different modified mathematical expressions, and the best combination of mathematical expression and solving method was used for hysteresis modeling. The parameters of the hysteresis model were determined based on the measured major hysteresis loop and first-order reversal curves. The model offers a simple determination of the magnetization procedure in the areas between measured curves, with the only correction of two parameters based on only two known points in the magnetization process. It was tested on two very different magnetic materials, and results show good agreement between the measured and calculated curves. The calculated curves between the measured curves have correct shapes. The main difference between our model and other models is that, in our model, each measured curve, major and reversal, is described with different parameters. The magnetization process between measured curves is described according to the nearest measured curve, and this ensures the best fit for each measured curve. In other models, there is mainly only one curve, a major hysteresis or magnetization curve, used for the determination of the parameters, and all other curves are then dependent on this curve. Results confirm that the evolutionary optimization method offers a reliable procedure for precise determination of the parameters.

show abstract

Gbest-Guided Covariance Matrix Adaptation Evolution Strategy for Large Scale Global Optimization

Zhang

Wang

2017

Intelligent Computing Theories and Application

View full text Add to dashboard Cite

Gbest guided differential evolution

Cited by 13 publications

References 15 publications

Innovative Approach for the Determination of a DC Motor’s and Drive’s Parameters Using Evolutionary Methods and Different Measured Current and Angular Speed Responses

Innovative Approach for the Determination of a DC Motor’s and Drive’s Parameters Using Evolutionary Methods and Different Measured Current and Angular Speed Responses

Determination of a Hysteresis Model Parameters with the Use of Different Evolutionary Methods for an Innovative Hysteresis Model

Gbest-Guided Covariance Matrix Adaptation Evolution Strategy for Large Scale Global Optimization

Contact Info

Product

Resources

About