工学的最適化：応用・手法・解析 Engineering Optimization : Applications, Methods and Analysis

著者名：Rhinehart, R. Russell

Wiley-ASME Press Series（2018/03/26発売）

• 言語：ENG
• ISBN：9781118936337
• eISBN：9781118936320

Description

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.

Table of Contents

Preface xix 

Acknowledgments xxvii 

Nomenclature xxix 

About the Companion Website xxxvii 

Section 1 Introductory Concepts 1 

1 Optimization: Introduction and Concepts 3 

2 Optimization Application Diversity and Complexity 33 

3 Validation: Knowing That the Answer Is Right 53 

Section 2 Univariate Search Techniques 59 

4 Univariate (Single DV) Search Techniques 61 

5 Path Analysis 93 

6 Stopping and Convergence Criteria: 1-D Applications 107 

Section 3 Multivariate Search Techniques 117 

7 Multidimension Application Introduction and the Gradient 119 

8 Elementary Gradient-Based Optimizers: CSLSandISD135 

9 Second-Order Model-Based Optimizers:SQandNR155 

10 Gradient-Based Optimizer Solutions:LM, RLM, CG, BFGS, RG, and GRG173 

11 Direct Search Techniques 187 

12 Linear Programming 223 

13 Dynamic Programming 233 

14 Genetic Algorithms and Evolutionary Computation 243 

15 Intuitive Optimization 253 

16 Surface Analysis II 257 

17 Convergence Criteria 2: N-D Applications 265 

18 Enhancements to Optimizers 271 

Section 4 Developing Your Application Statements 279 

19 Scaled Variables and Dimensional Consistency 281 

20 Economic Optimization 289 

21 Multiple OF and Constraint Applications 305 

22 Constraints 319 

23 Multiple Optima 335 

24 Stochastic Objective Functions 353 

25 Effects of Uncertainty 367 

26 Optimization of Probable Outcomes and Distribution Characteristics 381 

27 Discrete and Integer Variables 391 

28 Class Variables 397 

29 Regression 403 

Section 5 Perspective on Many Topics 441 

30 Perspective 443 

31 Response Surface Aberrations 459 

32 Identifying the Models, OF, DV, Convergence Criteria, and Constraints 475 

33 Evaluating Optimizers 489 

34 Troubleshooting Optimizers 499 

Section 6 Analysis of Leapfrogging Optimization 505 

35 Analysis of Leapfrogging 507 

Section 7 Case Studies 529 

36 Case Study 1: Economic Optimization of a Pipe System 531 

37 Case Study 2: Queuing Study 539 

38 Case Study 3: Retirement Study 543 

39 Case Study 4: AGoddard Rocket Study 551 

40 Case Study 5: Reservoir 557 

41 Case Study 6: Area Coverage 561 

42 Case Study 7: Approximating Series Solution to an ODE 565 

43 Case Study 8: Horizontal Tank Vapor–Liquid Separator 571 

44 Case Study 9: In Vitro Fertilization 579 

45 Case Study 10: Data Reconciliation 585 

Section 8 Appendices 591 

Section 9 References and Index 717 

References and Additional Resources 719 

Index 723