Parallel Efficient Global Optimization Method: A Novel Approach for Time-Dependent Reliability Analysis and Applications

Time-dependent reliability analysis requires the use of the extreme value of a response. The extreme value function is usually highly nonlinear, and traditional reliability methods, such as the first order reliability method (FORM), may produce large errors. The solution to this problem is using a surrogate model of the extreme response. The objective of this work is to improve the efficiency of building such a surrogate model. A mixed efficient global optimization (m-EGO) method is proposed. Different from the current EGO method, which draws samples of random variables and time independently, the m-EGO method draws samples for the two types of samples simultaneously. The m-EGO method employs the adaptive Kriging–Monte Carlo simulation (AK–MCS) so that high accuracy is also achieved. Then, Monte Carlo simulation (MCS) is applied to calculate the time-dependent reliability based on the surrogate model. Good accuracy and efficiency of the m-EGO method are demonstrated by three examples.

Download Full-text

Design of dimensionally stable composites using efficient global optimization method

Proceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications ◽

10.1177/1464420716664921 ◽

2016 ◽

Vol 233 (2) ◽

pp. 156-168 ◽

Cited By ~ 3

Author(s):

Levent Aydin ◽

Olgun Aydin ◽

H Seçil Artem ◽

Ali Mert

Keyword(s):

Global Optimization ◽

Communication Systems ◽

Optimization Problems ◽

Material Design ◽

Optimization Method ◽

Pattern Search ◽

Efficient Global Optimization ◽

Global Optimization Method ◽

Moisture Expansion ◽

Design And Optimization

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.

Download Full-text

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.

Download Full-text