Engineering optimization: applications, methods, and analysis
Rhinehart, R. Russell
Engineering optimization: applications, methods, and analysis - New Jersey Wiley India Pvt. Ltd. 2018 - xxxvii, 731 p.
TABLE OF CONTENTS
Section 1 Introductory Concepts
1 Optimization: Introduction and Concepts
2 Optimization Application Diversity and Complexity
3 Validation: Knowing That the Answer Is Right
Section 2 Univariate Search Techniques
4 Univariate (Single DV) Search Techniques
5 Path Analysis
6 Stopping and Convergence Criteria: 1-D Applications
Section 3 Multivariate Search Techniques
7 Multidimension Application Introduction and the Gradient
8 Elementary Gradient-Based Optimizers: CSLS and ISD
9 Second-Order Model-Based Optimizers: SQ and NR
10 Gradient-Based Optimizer Solutions: LM, RLM, CG, BFGS, RG, and GRG
11 Direct Search Techniques
12 Linear Programming
13 Dynamic Programming
14 Genetic Algorithms and Evolutionary Computation
15 Intuitive Optimization
16 Surface Analysis II
17 Convergence Criteria 2: N-D Applications
18 Enhancements to Optimizers
Section 4 Developing Your Application Statements
19 Scaled Variables and Dimensional Consistency
20 Economic Optimization
21 Multiple OF and Constraint Applications
22 Constraints
23 Multiple Optima
24 Stochastic Objective Functions
25 Effects of Uncertainty
26 Optimization of Probable Outcomes and Distribution Characteristics
27 Discrete and Integer Variables
28 Class Variables
29 Regression
Section 5 Perspective on Many Topics
30 Perspective
31 Response Surface Aberrations
32 Identifying the Models, OF, DV, Convergence Criteria, and Constraints
33 Evaluating Optimizers
34 Troubleshooting Optimizers
Section 6 Analysis of Leapfrogging Optimization
35 Analysis of Leapfrogging
Section 7 Case Studies
36 Case Study 1: Economic Optimization of a Pipe System
37 Case Study 2: Queuing Study
38 Case Study 3: Retirement Study
39 Case Study 4: A Goddard Rocket Study
40 Case Study 5: Reservoir
41 Case Study 6: Area Coverage
42 Case Study 7: Approximating Series Solution to an ODE
43 Case Study 8: Horizontal Tank Vapor–Liquid Separator
44 Case Study 9: In Vitro Fertilization
45 Case Study 10: Data Reconciliation
Section 8 Appendices
Appendix A Mathematical Concepts and Procedures
Appendix B Root Finding
Appendix C Gaussian Elimination
Appendix D Steady-State Identification in Noisy Signals
Appendix E Optimization Challenge Problems (2-D and Single OF)
Appendix F Brief on VBA Programming: Excel in Office 2013
An Application-Oriented Introduction to Essential Optimization Concepts and Best Practices Optimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. Engineering Optimization provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process. Although essential algorithms are explained in detail, the focus lies more in the human function: how to create an appropriate objective function, choose decision variables, identify and incorporate constraints, define convergence, and other critical issues that define the success or failure of an optimization project. Examples, exercises, and homework throughout reinforce the author’s “do, not study” approach to learning, underscoring the application-oriented discussion that provides a deep, generic understanding of the optimization process that can be applied to any field. Providing excellent reference for students or professionals, Engineering Optimization:
Describes and develops a variety of algorithms, including gradient based (such as Newton’s, and Levenberg-Marquardt), direct search (such as Hooke-Jeeves, Leapfrogging, and Particle Swarm), along with surrogate functions for surface characterization
Provides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values
Details current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling
Provides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book
Clear explanations, explicit equation derivations, and practical examples make this book ideal for use as part of a class or self-study, assuming a basic understanding of statistics, calculus, computer programming, and engineering models. Anyone seeking best practices for “making the best choices” will find value in this introductory resource.
9781118936337
Mathematical optimization
Engineering--Mathematical models
620.0015196 / RHI
Engineering optimization: applications, methods, and analysis - New Jersey Wiley India Pvt. Ltd. 2018 - xxxvii, 731 p.
TABLE OF CONTENTS
Section 1 Introductory Concepts
1 Optimization: Introduction and Concepts
2 Optimization Application Diversity and Complexity
3 Validation: Knowing That the Answer Is Right
Section 2 Univariate Search Techniques
4 Univariate (Single DV) Search Techniques
5 Path Analysis
6 Stopping and Convergence Criteria: 1-D Applications
Section 3 Multivariate Search Techniques
7 Multidimension Application Introduction and the Gradient
8 Elementary Gradient-Based Optimizers: CSLS and ISD
9 Second-Order Model-Based Optimizers: SQ and NR
10 Gradient-Based Optimizer Solutions: LM, RLM, CG, BFGS, RG, and GRG
11 Direct Search Techniques
12 Linear Programming
13 Dynamic Programming
14 Genetic Algorithms and Evolutionary Computation
15 Intuitive Optimization
16 Surface Analysis II
17 Convergence Criteria 2: N-D Applications
18 Enhancements to Optimizers
Section 4 Developing Your Application Statements
19 Scaled Variables and Dimensional Consistency
20 Economic Optimization
21 Multiple OF and Constraint Applications
22 Constraints
23 Multiple Optima
24 Stochastic Objective Functions
25 Effects of Uncertainty
26 Optimization of Probable Outcomes and Distribution Characteristics
27 Discrete and Integer Variables
28 Class Variables
29 Regression
Section 5 Perspective on Many Topics
30 Perspective
31 Response Surface Aberrations
32 Identifying the Models, OF, DV, Convergence Criteria, and Constraints
33 Evaluating Optimizers
34 Troubleshooting Optimizers
Section 6 Analysis of Leapfrogging Optimization
35 Analysis of Leapfrogging
Section 7 Case Studies
36 Case Study 1: Economic Optimization of a Pipe System
37 Case Study 2: Queuing Study
38 Case Study 3: Retirement Study
39 Case Study 4: A Goddard Rocket Study
40 Case Study 5: Reservoir
41 Case Study 6: Area Coverage
42 Case Study 7: Approximating Series Solution to an ODE
43 Case Study 8: Horizontal Tank Vapor–Liquid Separator
44 Case Study 9: In Vitro Fertilization
45 Case Study 10: Data Reconciliation
Section 8 Appendices
Appendix A Mathematical Concepts and Procedures
Appendix B Root Finding
Appendix C Gaussian Elimination
Appendix D Steady-State Identification in Noisy Signals
Appendix E Optimization Challenge Problems (2-D and Single OF)
Appendix F Brief on VBA Programming: Excel in Office 2013
An Application-Oriented Introduction to Essential Optimization Concepts and Best Practices Optimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. Engineering Optimization provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process. Although essential algorithms are explained in detail, the focus lies more in the human function: how to create an appropriate objective function, choose decision variables, identify and incorporate constraints, define convergence, and other critical issues that define the success or failure of an optimization project. Examples, exercises, and homework throughout reinforce the author’s “do, not study” approach to learning, underscoring the application-oriented discussion that provides a deep, generic understanding of the optimization process that can be applied to any field. Providing excellent reference for students or professionals, Engineering Optimization:
Describes and develops a variety of algorithms, including gradient based (such as Newton’s, and Levenberg-Marquardt), direct search (such as Hooke-Jeeves, Leapfrogging, and Particle Swarm), along with surrogate functions for surface characterization
Provides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values
Details current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling
Provides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book
Clear explanations, explicit equation derivations, and practical examples make this book ideal for use as part of a class or self-study, assuming a basic understanding of statistics, calculus, computer programming, and engineering models. Anyone seeking best practices for “making the best choices” will find value in this introductory resource.
9781118936337
Mathematical optimization
Engineering--Mathematical models
620.0015196 / RHI