Surrogate-Model Accelerated Random Search algorithm for global optimization with applications to inverse material identification

An optimization algorithm is proposed which is applicable for the global optimization of computationally expensive functions with specific applications in material identification. The methodology, referred to as the Surrogate-Model Accelerated Random Search (SMARS) algorithm, is a non-gradient based...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering Vol. 196; no. 45; pp. 4561 - 4576
Main Authors: Brigham, John C., Aquino, Wilkins
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 15.09.2007
Elsevier
Subjects:
Computational techniques
Exact sciences and technology
Finite elements
Fundamental areas of phenomenology (including applications)
Inverse problems
Mathematical methods in physics
Measurement and testing methods
Optimization
Physics
Random search
Solid mechanics
Static elasticity (thermoelasticity...)
Structural and continuum mechanics
Surrogate-model
Viscoelasticity
Surrogate-model
Viscoelasticity
Finite elements
Inverse problems
Optimization
Random search
Thermal diffusion
Local search
Probabilistic approach
Rheology
Optimization method
Iterative method
Global optimum
Neural network
Modeling
Search algorithm
Inverse problem
Functionnally graded material
Material testing
System identification
ISSN:0045-7825, 1879-2138
Online Access:Get full text
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Summary:An optimization algorithm is proposed which is applicable for the global optimization of computationally expensive functions with specific applications in material identification. The methodology, referred to as the Surrogate-Model Accelerated Random Search (SMARS) algorithm, is a non-gradient based iterative application of a random search algorithm and the surrogate-model method for optimization. The random search algorithm drives the global search portion of SMARS, thoroughly probing the search space to find optimal regions. The surrogate-model method then applies an artificial neural network to map local regions of the search space, and produce computationally inexpensive estimates to the solution, thereby accelerating the search. Through simulated examples, the SMARS algorithm is shown to be both robust and efficient. First, the minimization of a well known function with multiple local minima was considered to demonstrate the SMARS optimization capabilities with a known complex response surface. Then, two examples were considered for the inverse characterization of material properties. The identification of parameters of a rheological viscoelasticity model was considered first, and shows the SMARS algorithm’s tolerance to non-uniqueness over a large search space. Lastly, the identification of the distribution of thermal diffusivity for a functionally graded material was considered, and displays the SMARS capabilities to solve high-dimensional inverse problems. In all three examples, the performances of two traditional global search algorithms, a genetic algorithm and a random search algorithm, were compared to that of the SMARS algorithm. In all cases, the SMARS algorithm outperformed both traditional algorithms by attaining more accurate solutions with fewer function evaluations.
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ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2007.05.013