scholarly journals Optimal Design of Nonlinear Multimaterial Structures for Crashworthiness Using Cluster Analysis

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Kai Liu ◽  
Duane Detwiler ◽  
Andres Tovar
Keyword(s):  
Global Optimization ◽  
Conceptual Design ◽  
Optimization Algorithm ◽  
Linear Models ◽  
Learning Algorithm ◽  
Nonlinear Models ◽  
Expected Improvement ◽  
Design Algorithm ◽  
Optimal Material ◽  
Expected Improvement Function

This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, clustering, and metamodel-based global optimization. The conceptual design is generated using a structural optimization algorithm for linear models or a heuristic design algorithm for nonlinear models. Then, the conceptual design is clustered into a predefined number of clusters (materials) using a machine learning algorithm. Finally, the global optimization problem aims to find the optimal material parameters of the clustered design using metamodels. The metamodels are built using sampling and cross-validation and sequentially updated using an expected improvement function until convergence. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization (MTOP) method. The proposed approach is applied to a nonlinear, multi-objective design problems for crashworthiness.

scholarly journals Cluster-Based Optimization of Cellular Materials and Structures for Crashworthiness

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Kai Liu ◽  
Duane Detwiler ◽  
Andres Tovar
Keyword(s):  
Global Optimization ◽  
Structural Optimization ◽  
Conceptual Design ◽  
Nonlinear Models ◽  
Material Design ◽  
Cellular Materials ◽  
Cellular Material ◽  
Nonlinear Material ◽  
Optimization Approach ◽  
Material Models

The objective of this work is to establish a cluster-based optimization method for the optimal design of cellular materials and structures for crashworthiness, which involves the use of nonlinear, dynamic finite element models. The proposed method uses a cluster-based structural optimization approach consisting of four steps: conceptual design generation, clustering, metamodel-based global optimization, and cellular material design. The conceptual design is generated using structural optimization methods. K-means clustering is applied to the conceptual design to reduce the dimensional of the design space as well as define the internal architectures of the multimaterial structure. With reduced dimension space, global optimization aims to improve the crashworthiness of the structure can be performed efficiently. The cellular material design incorporates two homogenization methods, namely, energy-based homogenization for linear and nonlinear elastic material models and mean-field homogenization for (fully) nonlinear material models. The proposed methodology is demonstrated using three designs for crashworthiness that include linear, geometrically nonlinear, and nonlinear models.

scholarly journals Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Kai Liu ◽  
Tong Wu ◽  
Duane Detwiler ◽  
Jitesh Panchal ◽  
Andres Tovar
Keyword(s):  
Cluster Analysis ◽  
Structural Optimization ◽  
Conceptual Design ◽  
Design Space ◽  
Material Selection ◽  
Bayesian Optimization ◽  
Expected Improvement ◽  
Cluster Validity Index ◽  
Sizing Optimization ◽  
Expected Improvement Function

Abstract This work introduces a cluster-based structural optimization (CBSO) method for the design of categorical multimaterial structures subjected to crushing, dynamic loading. The proposed method consists of three steps: conceptual design generation, design clustering, and Bayesian optimization. In the first step, a conceptual design is generated using the hybrid cellular automaton (HCA) algorithm. In the second step, threshold-based cluster analysis yields a lower-dimensional design. Here, a cluster validity index for structural optimization is introduced in order to qualitatively evaluate the clustered design. In the third step, the optimal design is obtained through Bayesian optimization, minimizing a constrained expected improvement function. This function allows to impose soft constraints by properly redefining the expected improvement based on the maximum constraint violation. The Bayesian optimization algorithm implemented in this work has the ability to search over (i) a real design space for sizing optimization, (ii) a categorical design space for material selection, or (iii) a mixed design space for concurrent sizing optimization and material selection. With the proposed method, materials are optimally selected based on multiple attributes and multiple objectives without the need for material ranking. The effectiveness of this approach is demonstrated with the design for crashworthiness of multimaterial plates and thin-walled structures.

scholarly journals A Hybrid Whale Optimization Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1477
Author(s):  
Chun-Yao Lee ◽  
Guang-Lin Zhuo
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Initial Population ◽  
Test Functions ◽  
Benchmark Test ◽  
Thermal Exchange ◽  
Whale Optimization ◽  
Benchmark Test Functions

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

Pinhole-imaging-based learning butterfly optimization algorithm for global optimization and feature selection

2021 ◽  
Vol 103 ◽  
pp. 107146
Author(s):  
Wen Long ◽  
Jianjun Jiao ◽  
Ximing Liang ◽  
Tiebin Wu ◽  
Ming Xu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Global Optimization ◽  
Optimization Algorithm ◽  
Pinhole Imaging

scholarly journals Chaos-enhanced moth-flame optimization algorithm for global optimization

2019 ◽  
Vol 30 (6) ◽  
pp. 1144-1159 ◽  
Author(s):  
Hongwei LI ◽  
Jianyong LIU ◽  
Liang CHEN ◽  
Jingbo BAI ◽  
Yangyang SUN ◽  
...  
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Moth Flame Optimization Algorithm

DEFORMABLE MESH FOR AUTOMATED SURFACE EXTRACTION FROM NOISY IMAGES

2004 ◽  
Vol 04 (03) ◽  
pp. 405-432 ◽  
Author(s):  
JUSSI TOHKA ◽  
JOUNI M. MYKKÄNEN
Keyword(s):  
Global Optimization ◽  
Internal Energy ◽  
Optimization Algorithm ◽  
Automatic Segmentation ◽  
Medical Image Analysis ◽  
Surface Extraction ◽  
Deformable Surface ◽  
Surface Models ◽  
Deformable Mesh ◽  
Parameter Values

Surface extraction from noisy volumetric images is a problem commonly encountered in medical image analysis. Deformable surface models can, in principle, solve the problem in an automatic manner. However, it is often essential that a reasonably close initialization and good parameter values for deformable models are provided. In this paper, novel algorithms for global minimization of the energy of deformable meshes are presented. We demonstrate that global optimization by these algorithms reduces the sensitivity of the deformable mesh to its initialization and its parameter values. Consequently, it becomes easier to automate the initialization process and the selection of parameter values. As the second contribution, the internal energy function is derived in a novel way in the framework of deformable surface models. The construction of the internal energy in this way features a simple way to derive the variants of our global optimization algorithm. The experiments with synthetic images are performed to compare variants of the proposed optimization algorithm. Also, we present a practical application of our deformable model to automatic segmentation of positron emission tomography images.

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