Engineering Design Optimization

2021 ◽  
Author(s):  
Joaquim R. R. A. Martins ◽  
Andrew Ning
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Numerical Optimization ◽  
Chemical Engineering ◽  
Optimization Theory ◽  
Graduate Course ◽  
Wide Range ◽  
Engineering Design Optimization ◽  
Practical Engineering ◽  
Gradient Based

Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 400 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice. Accompanied online by a solutions manual for instructors and source code for problems, this is ideal for a one- or two-semester graduate course on optimization in aerospace, civil, mechanical, electrical, and chemical engineering departments.

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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