Automotive Crashworthiness Design Optimization Based on Efficient Global Optimization Method

Dimensionally stable material design is an important issue for space structures such as space laser communication systems, telescopes, and satellites. Suitably designed composite materials for this purpose can meet the functional and structural requirements. In this paper, it is aimed to design the dimensionally stable laminated composites by using efficient global optimization method. For this purpose, the composite plate optimization problems have been solved for high stiffness and low coefficients of thermal and moisture expansion. Some of the results based on efficient global optimization solution have been verified by genetic algorithm, simulated annealing, and generalized pattern search solutions from the previous studies. The proposed optimization algorithm is also validated experimentally. After completing the design and optimization process, failure analysis of the optimized composites has been performed based on Tsai–Hill, Tsai–Wu, Hoffman, and Hashin–Rotem criteria.

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A New Global Optimization Method for Simultaneous Computation on Expensive Black-Box Functions

Volume 2: 29th Design Automation Conference, Parts A and B ◽

10.1115/detc2003/dac-48763 ◽

2003 ◽

Author(s):

Liqun Wang ◽

Songqing Shan ◽

G. Gary Wang

Keyword(s):

Global Optimization ◽

Optimization Problems ◽

Optimization Methods ◽

Search Space ◽

Optimization Method ◽

Global Optimum ◽

Black Box ◽

Efficient Global Optimization ◽

Global Optimization Method ◽

Global Optimization Methods

The presence of black-box functions in engineering design, which are usually computation-intensive, demands efficient global optimization methods. This work proposes a new global optimization method for black-box functions. The global optimization method is based on a novel mode-pursuing sampling (MPS) method which systematically generates more sample points in the neighborhood of the function mode while statistically covers the entire search space. Quadratic regression is performed to detect the region containing the global optimum. The sampling and detection process iterates until the global optimum is obtained. Through intensive testing, this method is found to be effective, efficient, robust, and applicable to both continuous and discontinuous functions. It supports simultaneous computation and applies to both unconstrained and constrained optimization problems. Because it does not call any existing global optimization tool, it can be used as a standalone global optimization method for inexpensive problems as well. Limitation of the method is also identified and discussed.

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SADEA-II: A generalized method for efficient global optimization of antenna design

Journal of Computational Design and Engineering ◽

10.1016/j.jcde.2016.11.002 ◽

2016 ◽

Vol 4 (2) ◽

pp. 86-97 ◽

Cited By ~ 9

Author(s):

Bo Liu ◽

Slawomir Koziel ◽

Nazar Ali

Keyword(s):

Data Mining ◽

Global Optimization ◽

Design Optimization ◽

Surrogate Model ◽

Simulation Models ◽

Optimization Methods ◽

Optimization Method ◽

Antenna Design ◽

Efficient Global Optimization ◽

Optimization Framework

Abstract Efficiency improvement is of great significance for simulation-driven antenna design optimization methods based on evolutionary algorithms (EAs). The two main efficiency enhancement methods exploit data-driven surrogate models and/or multi-fidelity simulation models to assist EAs. However, optimization methods based on the latter either need ad hoc low-fidelity model setup or have difficulties in handling problems with more than a few design variables, which is a main barrier for industrial applications. To address this issue, a generalized three stage multi-fidelity-simulation-model assisted antenna design optimization framework is proposed in this paper. The main ideas include introduction of a novel data mining stage handling the discrepancy between simulation models of different fidelities, and a surrogate-model-assisted combined global and local search stage for efficient high-fidelity simulation model-based optimization. This framework is then applied to SADEA, which is a state-of-the-art surrogate-model-assisted antenna design optimization method, constructing SADEA-II. Experimental results indicate that SADEA-II successfully handles various discrepancy between simulation models and considerably outperforms SADEA in terms of computational efficiency while ensuring improved design quality. Highlights An EFFICIENT antenna design global optimization method for problems requiring very expensive EM simulations. A new multi-fidelity surrogate-model-based optimization framework to perform RELIABLE efficient global optimization A data mining method to address distortions of EM models of different fidelities (bottleneck of multi-fidelity design).

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