scholarly journals Development of optimization methods to deal with current challenges in engineering design optimization

2014 ◽  
Vol 29 (1) ◽  
pp. 219-221 ◽  
Author(s):  
Hemant Kumar Singh
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Optimization Methods ◽  
Engineering Design Optimization

Generic Classes of Iterative Behavior in Engineering Design Optimization

1990 ◽  
Author(s):  
Scott A. Burns
Keyword(s):  
Phase Space ◽  
Design Optimization ◽  
Engineering Design ◽  
Optimization Methods ◽  
Basins Of Attraction ◽  
Engineering Design Optimization ◽  
Phase Space Portrait

Abstract The iterative dynamics of engineering design optimization processes are considered and generic features are identified. Two optimization methods are applied to the design of an elastic grillage structure and basins of attraction are produced to locate both simple and cyclic attractors. A phase-space portrait is constructed to visualize a complex non-cyclic attractor.

A hybrid global optimization method based on multiple metamodels

2018 ◽  
Vol 35 (1) ◽  
pp. 71-90 ◽  
Author(s):  
Xiwen Cai ◽  
Haobo Qiu ◽  
Liang Gao ◽  
Xiaoke Li ◽  
Xinyu Shao
Keyword(s):  
Global Optimization ◽  
Design Optimization ◽  
Engineering Design ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Expected Improvement ◽  
Benchmark Problems ◽  
Content Type ◽  
Engineering Design Optimization

Purpose This paper aims to propose hybrid global optimization based on multiple metamodels for improving the efficiency of global optimization. Design/methodology/approach The method has fully utilized the information provided by different metamodels in the optimization process. It not only imparts the expected improvement criterion of kriging into other metamodels but also intelligently selects appropriate metamodeling techniques to guide the search direction, thus making the search process very efficient. Besides, the corresponding local search strategies are also put forward to further improve the optimizing efficiency. Findings To validate the method, it is tested by several numerical benchmark problems and applied in two engineering design optimization problems. Moreover, an overall comparison between the proposed method and several other typical global optimization methods has been made. Results show that the global optimization efficiency of the proposed method is higher than that of the other methods for most situations. Originality/value The proposed method sufficiently utilizes multiple metamodels in the optimizing process. Thus, good optimizing results are obtained, showing great applicability in engineering design optimization problems which involve costly simulations.

A rank-based constraint handling technique for engineering design optimization problems solved by genetic algorithms

2017 ◽  
Vol 187 ◽  
pp. 77-87 ◽  
Author(s):  
Rafael de Paula Garcia ◽  
Beatriz Souza Leite Pires de Lima ◽  
Afonso Celso de Castro Lemonge ◽  
Breno Pinheiro Jacob
Keyword(s):  
Genetic Algorithms ◽  
Design Optimization ◽  
Engineering Design ◽  
Optimization Problems ◽  
Constraint Handling ◽  
Engineering Design Optimization ◽  
Handling Technique

Optimal Design With a Monomial-Based Version of Newton’s Method

1991 ◽  
Author(s):  
Scott A. Burns
Keyword(s):  
Optimal Design ◽  
Design Optimization ◽  
Engineering Design ◽  
Nonlinear Equations ◽  
Newton’S Method ◽  
Newton's Method ◽  
Geometric Mean ◽  
System Of Nonlinear Equations ◽  
Engineering Design Optimization

Abstract A monomial-based method for solving systems of algebraic nonlinear equations is presented. The method uses the arithmetic-geometric mean inequality to construct a system of monomial equations that approximates the system of nonlinear equations. This “monomial method” is closely related to Newton’s method, yet exhibits many special properties not shared by Newton’s method that enhance performance. These special properties are discussed in relation to engineering design optimization.

A lower confidence bounding approach based on the coefficient of variation for expensive global design optimization

2019 ◽  
Vol 36 (3) ◽  
pp. 830-849 ◽  
Author(s):  
Ji Cheng ◽  
Ping Jiang ◽  
Qi Zhou ◽  
Jiexiang Hu ◽  
Tao Yu ◽  
...  
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Computational Efficiency ◽  
Coefficient Of Variation ◽  
Computational Simulations ◽  
Exploration And Exploitation ◽  
Content Type ◽  
Degree Of Dispersion ◽  
Lower Confidence ◽  
Engineering Design Optimization

PurposeEngineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the adaptive metamodel-based design optimization (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, a lower confidence bounding approach based on the coefficient of variation (CV-LCB) approach, to balance the exploration and exploitation objectively for obtaining a global optimum under limited computational budget.Design/methodology/approachIn the proposed CV-LCB approach, the coefficient of variation (CV) of predicted values is introduced to indicate the degree of dispersion of objective function values, while the CV of predicting errors is introduced to represent the accuracy of the established metamodel. Then, a weighted formula, which takes the degree of dispersion and the prediction accuracy into consideration, is defined based on the already-acquired CV information to adaptively update the metamodel during the optimization process.FindingsTen numerical examples with different degrees of complexity and an AIAA aerodynamic design optimization problem are used to demonstrate the effectiveness of the proposed CV-LCB approach. The comparisons between the proposed approach and four existing approaches regarding the computational efficiency and robustness are made. Results illustrate the merits of the proposed CV-LCB approach in computational efficiency and robustness.Practical implicationsThe proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations.Originality/valueCV-LCB approach can balance the exploration and exploitation objectively.

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