Characterization of Piezoelectric Material Parameters Through a Global Optimization Algorithm

2020 ◽  
Vol 45 (2) ◽  
pp. 480-488
Author(s):  
Marcus Wild ◽  
Martin Bring ◽  
Lars Hoff ◽  
Karina Hjelmervik
Keyword(s):  
Global Optimization ◽  
Piezoelectric Material ◽  
Optimization Algorithm ◽  
Global Optimization Algorithm ◽  
Material Parameters

A New Global Optimization Algorithm and its Application

2008 ◽  
Vol 33-37 ◽  
pp. 1407-1412
Author(s):  
Ying Hui Lu ◽  
Shui Lin Wang ◽  
Hao Jiang ◽  
Xiu Run Ge
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Inverse Analysis ◽  
Optimization Problem ◽  
Practical Application ◽  
Global Optimization Algorithm ◽  
Material Parameters ◽  
Non Linear ◽  
Variable Space

In geotechnical engineering, based on the theory of inverse analysis of displacement, the problem for identification of material parameters can be transformed into an optimization problem. Commonly, because of the non-linear relationship between the identified parameters and the displacement, the objective function bears the multimodal characteristic in the variable space. So to solve better the multimodal characteristic in the non-linear inverse analysis, a new global optimization algorithm, which integrates the dynamic descent algorithm and the modified BFGS (Brogden-Fletcher-Goldfrab-Shanno) algorithm, is proposed. Five typical multimodal functions in the variable space are tested to prove that the new proposed algorithm can quickly converge to the best point with few function evaluations. In the practical application, the new algorithm is employed to identify the Young’s modulus of four different materials. The results of the identification further show that the new proposed algorithm is a very highly efficient and robust one.

A New Optimization Algorithm for Inverse Analysis to Material Parameters

2008 ◽  
Vol 575-578 ◽  
pp. 1013-1019
Author(s):  
Ying Hui Lu ◽  
Shui Lin Wang ◽  
Hao Jiang
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Linear Correlation ◽  
Inverse Analysis ◽  
High Efficiency ◽  
Global Optimization Algorithm ◽  
Local Optima ◽  
Material Parameters ◽  
Non Linear

the inverse analysis to material parameters is often translated into an optimization for an objective function, based on the correlation between the material parameters and the foregone information. But mostly because of the non-linear correlation, a good optimization algorithm with the capabilities to avoid being trapped by local optima is required during the process of optimization. So the present paper proposes a new global optimization algorithm, which couples the dynamic canonical descent algorithm and the improved Powell’s algorithm. The high efficiency of the new algorithm is shown on four known problems classically for testing optimization algorithms and finally, in the non-linear inverse analysis, the new algorithm is used for optimizing an objective function to get material parameters rightly.

A global optimization algorithm for solving a four-person game

2017 ◽  
Vol 13 (3) ◽  
pp. 587-596
Author(s):  
S. Batbileg ◽  
N. Tungalag ◽  
A. Anikin ◽  
A. Gornov ◽  
E. Finkelstein
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Global Optimization Algorithm ◽  
Person Game
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