Engineering optimization : applications, methods, and analysis

著者

• Rhinehart, R. Russell

書誌事項

Engineering optimization : applications, methods, and analysis

R. Russell Rhinehart

（Wiley-ASME Press series）

Wiley , ASME Press, 2018

• : [hardback]

注記

"This work is a co-publication between ASME Press and John Wiley & Sons Ltd."

Includes bibliographical references (p. 719-721) and index

内容説明・目次

内容説明

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.

目次

Contents Preface xix Acknowledgments xxvii Nomenclature xxix About the Companion Website xxxvii Section 1 Introductory Concepts 1 1 Optimization: Introduction and Concepts 3 1.1 Optimization and Terminology 3 1.2 Optimization Concepts and Definitions 4 1.3 Examples 6 1.4 Terminology Continued 10 1.4.1 Constraint 10 1.4.2 Feasible Solutions 10 1.4.3 Minimize or Maximize 11 1.4.4 Canonical Form of the Optimization Statement 11 1.5 Optimization Procedure 12 1.6 Issues That Shape Optimization Procedures 16 1.7 Opposing Trends 17 1.8 Uncertainty 20 1.9 Over- and Under-specification in Linear Equations 21 1.10 Over- and Under-specification in Optimization 22 1.11 Test Functions 23 1.12 Significant Dates in Optimization 23 1.13 Iterative Procedures 26 1.14 Takeaway 27 1.15 Exercises 27 2 Optimization Application Diversity and Complexity 33 2.1 Optimization 33 2.2 Nonlinearity 33 2.3 Min, Max, Min-Max, Max-Min, ... 34 2.4 Integers and Other Discretization 35 2.5 Conditionals and Discontinuities: Cliffs Ridges/Valleys 36 2.6 Procedures, Not Equations 37 2.7 Static and Dynamic Models 38 2.8 Path Integrals 38 2.9 Economic Optimization and Other Nonadditive Cost Functions 38 2.10 Reliability 39 2.11 Regression 40 2.12 Deterministic and Stochastic 42 2.13 Experimental w.r.t. Modeled OF 43 2.14 Single and Multiple Optima 44 2.15 Saddle Points 45 2.16 Inflections 46 2.17 Continuum and Discontinuous DVs 47 2.18 Continuum and Discontinuous Models 47 2.19 Constraints and Penalty Functions 48 2.20 Ranks and Categorization: Discontinuous OFs 50 2.21 Underspecified OFs 51 2.22 Takeaway 51 2.23 Exercises 51 3 Validation: Knowing That the Answer Is Right 53 3.1 Introduction 53 3.2 Validation 53 3.3 Advice on Becoming Proficient 55 3.4 Takeaway 56 3.5 Exercises 57 Section 2 Univariate Search Techniques 59 4 Univariate (Single DV) Search Techniques 61 4.1 Univariate (Single DV) 61 4.2 Analytical Method of Optimization 62 4.2.1 Issues with the Analytical Approach 63 4.3 Numerical Iterative Procedures 64 4.3.1 Newton's Methods 64 4.3.2 Successive Quadratic (A Surrogate Model or Approximating Model Method) 68 4.4 Direct Search Approaches 70 4.4.1 Bisection Method 70 4.4.2 Golden Section Method 72 4.4.3 Perspective at This Point 74 4.4.4 Heuristic Direct Search 74 4.4.5 Leapfrogging 76 4.4.6 LF for Stochastic Functions 79 4.5 Perspectives on Univariate Search Methods 82 4.6 Evaluating Optimizers 85 4.7 Summary of Techniques 85 4.7.1 Analytical Method 86 4.7.2 Newton's (and Variants Like Secant) 86 4.7.3 Successive Quadratic 86 4.7.4 Golden Section Method 86 4.7.5 Heuristic Direct 87 4.7.6 Leapfrogging 87 4.8 Takeaway 87 4.9 Exercises 88 5 Path Analysis 93 5.1 Introduction 93 5.2 Path Examples 93 5.3 Perspective About Variables 96 5.4 Path Distance Integral 97 5.5 Accumulation along a Path 99 5.6 Slope along a Path 101 5.7 Parametric Path Notation 103 5.8 Takeaway 104 5.9 Exercises 104 6 Stopping and Convergence Criteria: 1-D Applications 107 6.1 Stopping versus Convergence Criteria 107 6.2 Determining Convergence 107 6.2.1 Threshold on the OF 108 6.2.2 Threshold on the Change in the OF 108 6.2.3 Threshold on the Change in the DV 108 6.2.4 Threshold on the Relative Change in the DV 109 6.2.5 Threshold on the Relative Change in the OF 109 6.2.6 Threshold on the Impact of the DV on the OF 109 6.2.7 Convergence Based on Uncertainty Caused by the Givens 109 6.2.8 Multiplayer Range 110 6.2.9 Steady-State Convergence 110 6.3 Combinations of Convergence Criteria 111 6.4 Choosing Convergence Threshold Values 112 6.5 Precision 112 6.6 Other Convergence Criteria 113 6.7 Stopping Criteria to End a Futile Search 113 6.7.1 N Iteration Threshold 114 6.7.2 Execution Error 114 6.7.3 Constraint Violation 114 6.8 Choices! 114 6.9 Takeaway 114 6.10 Exercises 115 Section 3 Multivariate Search Techniques 117 7 Multidimension Application Introduction and the Gradient 119 7.1 Introduction 119 7.2 Illustration of Surface and Terms 122 7.3 Some Surface Analysis 123 7.4 Parametric Notation 128 7.5 Extension to Higher Dimension 130 7.6 Takeaway 131 7.7 Exercises 131 8 Elementary Gradient-Based Optimizers: CSLS and ISD 135 8.1 Introduction 135 8.2 Cauchy's Sequential Line Search 135 8.2.1 CSLS with Successive Quadratic 137 8.2.2 CSLS with Newton/Secant 138 8.2.3 CSLS with Golden Section 138 8.2.4 CSLS with Leapfrogging 138 8.2.5 CSLS with Heuristic Direct Search 139 8.2.6 CSLS Commentary 139 8.2.7 CSLS Pseudocode 140 8.2.8 VBA Code for a 2-DV Application 141 8.3 Incremental Steepest Descent 144 8.3.1 Pseudocode for the ISD Method 144 8.3.2 Enhanced ISD 145 8.3.3 ISD Code 148 8.4 Takeaway 149 8.5 Exercises 149 9 Second-Order Model-Based Optimizers: SQ and NR 155 9.1 Introduction 155 9.2 Successive Quadratic 155 9.2.1 Multivariable SQ 156 9.2.2 SQ Pseudocode 159 9.3 Newton-Raphson 159 9.3.1 NR Pseudocode 162 9.3.2 Attenuate NR 163 9.3.3 Quasi-Newton 166 9.4 Perspective on CSLS, ISD, SQ, and NR 168 9.5 Choosing Step Size for Numerical Estimate of Derivatives 169 9.6 Takeaway 170 9.7 Exercises 170 10 Gradient-Based Optimizer Solutions: LM, RLM, CG, BFGS, RG, and GRG 173 10.1 Introduction 173 10.2 Levenberg-Marquardt (LM) 173 10.2.1 LM VBA Code for a 2-DV Case 175 10.2.2 Modified LM (RLM) 176 10.2.3 RLM Pseudocode 177 10.2.4 RLM VBA Code for a 2-DV Case 178 10.3 Scaled Variables 180 10.4 Conjugate Gradient (CG) 182 10.5 Broyden-Fletcher-Goldfarb-Shanno (BFGS) 183 10.6 Generalized Reduced Gradient (GRG) 184 10.7 Takeaway 186 10.8 Exercises 186 11 Direct Search Techniques 187 11.1 Introduction 187 11.2 Cyclic Heuristic Direct (CHD) Search 188 11.2.1 CHD Pseudocode 188 11.2.2 CHD VBA Code 189 11.3 Hooke-Jeeves (HJ) 192 11.3.1 HJ Code in VBA 195 11.4 Compare and Contrast CHD and HJ Features: A Summary 197 11.5 Nelder-Mead (NM) Simplex: Spendley, Hext, and Himsworth 199 11.6 Multiplayer Direct Search Algorithms 200 11.7 Leapfrogging 201 11.7.1 Convergence Criteria 208 11.7.2 Stochastic Surfaces 209 11.7.3 Summary 209 11.8 Particle Swarm Optimization 209 11.8.1 Individual Particle Behavior 210 11.8.2 Particle Swarm 213 11.8.3 PSO Equation Analysis 215 11.9 Complex Method (CM) 216 11.10 A Brief Comparison 217 11.11 Takeaway 218 11.12 Exercises 219 12 Linear Programming 223 12.1 Introduction 223 12.2 Visual Representation and Concepts 225 12.3 Basic LP Procedure 228 12.4 Canonical LP Statement 228 12.5 LP Algorithm 229 12.6 Simplex Tableau 230 12.7 Takeaway 231 12.8 Exercises 231 13 Dynamic Programming 233 13.1 Introduction 233 13.2 Conditions 236 13.3 DP Concept 237 13.4 Some Calculation Tips 240 13.5 Takeaway 241 13.6 Exercises 241 14 Genetic Algorithms and Evolutionary Computation 243 14.1 Introduction 243 14.2 GA Procedures 243 14.3 Fitness of Selection 245 14.4 Takeaway 250 14.5 Exercises 250 15 Intuitive Optimization 253 15.1 Introduction 253 15.2 Levels 254 15.3 Takeaway 254 15.4 Exercises 254 16 Surface Analysis II 257 16.1 Introduction 257 16.2 Maximize Is Equivalent to Minimize the Negative 257 16.3 Scaling by a Positive Number Does Not Change DV 258 16.4 Scaled and Translated OFs Do Not Change DV 258 16.5 Monotonic Function Transformation Does Not Change DV 258 16.6 Impact on Search Path or NOFE 261 16.7 Inequality Constraints 263 16.8 Transforming DVs 263 16.9 Takeaway 263 16.10 Exercises 263 17 Convergence Criteria 2: N-D Applications 265 17.1 Introduction 265 17.2 Defining an Iteration 265 17.3 Criteria for Single TS Deterministic Procedures 266 17.4 Criteria for Multiplayer Deterministic Procedures 267 17.5 Stochastic Applications 268 17.7 Takeaway 269 17.8 Exercises 269 18 Enhancements to Optimizers 271 18.1 Introduction 271 18.2 Criteria for Replicate Trials 271 18.3 Quasi-Newton 274 18.4 Coarse-Fine Sequence 275 18.5 Number of Players 275 18.6 Search Range Adjustment 276 18.7 Adjustment of Optimizer Coefficient Values or Options in Process 276 18.8 Initialization Range 277 18.9 OF and DV Transformations 277 18.10 Takeaway 278 18.11 Exercises 278 Section 4 Developing Your Application Statements 279 19 Scaled Variables and Dimensional Consistency 281 19.1 Introduction 281 19.2 A Scaled Variable Approach 283 19.3 Sampling of Issues with Primitive Variables 283 19.4 Linear Scaling Options 285 19.5 Nonlinear Scaling 286 19.6 Takeaway 287 19.7 Exercises 287 20 Economic Optimization 289 20.1 Introduction 289 20.2 Annual Cash Flow 290 20.3 Including Risk as an Annual Expense 291 20.4 Capital 293 20.5 Combining Capital and Nominal Annual Cash Flow 293 20.6 Combining Time Value and Schedule of Capital and Annual Cash Flow 296 20.7 Present Value 297 20.8 Including Uncertainty 298 20.8.1 Uncertainty Models 301 20.8.2 Methods to Include Uncertainty in an Optimization 303 20.9 Takeaway 304 20.10 Exercises 304 21 Multiple OF and Constraint Applications 305 21.1 Introduction 305 21.2 Solution 1: Additive Combinations of the Functions 306 21.2.1 Solution 1a: Classic Weighting Factors 307 21.2.2 Solution 1b: Equal Concern Weighting 307 21.2.3 Solution 1c: Nonlinear Weighting 309 21.3 Solution 2: Nonadditive OF Combinations 311 21.4 Solution 3: Pareto Optimal 311 21.5 Takeaway 316 21.6 Exercises 316 22 Constraints 319 22.1 Introduction 319 22.2 Equality Constraints 320 22.2.1 Explicit Equality Constraints 320 22.2.2 Implicit Equality Constraints 321 22.3 Inequality Constraints 321 22.3.1 Penalty Function: Discontinuous 323 22.3.2 Penalty Function: Soft Constraint 323 22.3.3 Inequality Constraints: Slack and Surplus Variables 325 22.4 Constraints: Pass/Fail Categories 329 22.5 Hard Constraints Can Block Progress 330 22.6 Advice 331 22.7 Constraint-Equivalent Features 332 22.8 Takeaway 332 22.9 Exercises 332 23 Multiple Optima 335 23.1 Introduction 335 23.2 Solution: Multiple Starts 337 23.2.1 A Priori Method 340 23.2.2 A Posteriori Method 342 23.2.3 Snyman and Fatti Criterion A Posteriori Method 345 23.3 Other Options 348 23.4 Takeaway 349 23.5 Exercises 350 24 Stochastic Objective Functions 353 24.1 Introduction 353 24.2 Method Summary for Optimizing Stochastic Functions 356 24.2.1 Step 1: Replicate the Apparent Best Player 356 24.2.2 Step 2: Steady-State Detection 357 24.3 What Value to Report? 358 24.4 Application Examples 359 24.4.1 GMC Control of Hot and Cold Mixing 359 24.4.2 MBC of Hot and Cold Mixing 359 24.4.3 Batch Reaction Management 359 24.4.4 Reservoir and Stochastic Boot Print 361 24.4.5 Optimization Results 362 24.5 Takeaway 365 24.6 Exercises 365 25 Effects of Uncertainty 367 25.1 Introduction 367 25.2 Sources of Error and Uncertainty 368 25.3 Significant Digits 370 25.4 Estimating Uncertainty on Values 371 25.5 Propagating Uncertainty on DV Values 372 25.5.1 Analytical Method 373 25.5.2 Numerical Method 375 25.6 Implicit Relations 378 25.7 Estimating Uncertainty in DV and OF 378 25.8 Takeaway 379 25.9 Exercises 379 26 Optimization of Probable Outcomes and Distribution Characteristics 381 26.1 Introduction 381 26.2 The Concept of Modeling Uncertainty 385 26.3 Stochastic Approach 387 26.4 Takeaway 389 26.5 Exercises 389 27 Discrete and Integer Variables 391 27.1 Introduction 391 27.2 Optimization Solutions 394 27.2.1 Exhaustive Search 394 27.2.2 Branch and Bound 394 27.2.3 Cyclic Heuristic 394 27.2.4 Leapfrogging or Other Multiplayer Search 395 27.3 Convergence 395 27.4 Takeaway 395 27.5 Exercises 395 28 Class Variables 397 28.1 Introduction 397 28.2 The Random Keys Method: Sequence 398 28.3 The Random Keys Method: Dichotomous Variables 400 28.4 Comments 401 28.5 Takeaway 401 28.6 Exercises 401 29 Regression 403 29.1 Introduction 403 29.2 Perspective 404 29.3 Least Squares Regression: Traditional View on Linear Model Parameters 404 29.4 Models Nonlinear in DV 405 29.4.1 Models with a Delay 407 29.5 Maximum Likelihood 408 29.5.1 Akaho's Method 411 29.6 Convergence Criterion 416 29.7 Model Order or Complexity 421 29.8 Bootstrapping to Reveal Model Uncertainty 425 29.8.1 Interpretation of Bootstrapping Analysis 428 29.8.2 Appropriating Bootstrapping 430 29.9 Perspective 431 29.10 Takeaway 431 29.11 Exercises 432 Section 5 Perspective on Many Topics 441 30 Perspective 443 30.1 Introduction 443 30.2 Classifications 443 30.3 Elements Associated with Optimization 445 30.4 Root Finding Is Not Optimization 446 30.5 Desired Engineering Attributes 446 30.6 Overview of Optimizers and Attributes 447 30.6.1 Gradient Based: Cauchy Sequential Line Search, Incremental Steepest Descent, GRG, Etc. 447 30.6.2 Local Surface Characterization Based: Newton-Raphson, Levenberg-Marquardt, Successive Quadratic, RLM, Quasi-Newton, Etc. 448 30.6.3 Direct Search with Single Trial Solution: Cyclic Heuristic, Hooke-Jeeves, and Nelder-Mead 448 30.6.4 Multiplayer Direct Search Optimizers: Leapfrogging, Particle Swarm, and Genetic Algorithms 448 30.7 Choices 448 30.8 Variable Classifications 449 30.8.1 Nominal 449 30.8.2 Ordinal 450 30.8.3 Cardinal 450 30.9 Constraints 451 30.10 Takeaway 453 30.11 Exercises 453 31 Response Surface Aberrations 459 31.1 Introduction 459 31.2 Cliffs (Vertical Walls) 459 31.3 Sharp Valleys (or Ridges) 459 31.4 Striations 463 31.5 Level Spots (Functions 1, 27, 73, 84) 463 31.6 Hard-to-Find Optimum 466 31.7 Infeasible Calculations 468 31.8 Uniform Minimum 468 31.9 Noise: Stochastic Response 469 31.10 Multiple Optima 471 31.11 Takeaway 473 31.12 Exercises 473 32 Identifying the Models, OF, DV, Convergence Criteria, and Constraints 475 32.1 Introduction 475 32.2 Evaluate the Results 476 32.3 Takeaway 482 32.4 Exercises 482 33 Evaluating Optimizers 489 33.1 Introduction 489 33.2 Challenges to Optimizers 490 33.3 Stakeholders 490 33.4 Metrics of Optimizer Performance 490 33.5 Designing an Experimental Test 492 33.6 Takeaway 495 33.7 Exercises 496 34 Troubleshooting Optimizers 499 34.1 Introduction 499 34.2 DV Values Do Not Change 499 34.3 Multiple DV Values for the Same OF Value 499 34.4 EXE Error 500 34.5 Extreme Values 500 34.6 DV Is Dependent on Convergence Threshold 500 34.7 OF Is Irreproducible 501 34.8 Concern over Results 501 34.9 CDF Features 501 34.10 Parameter Correlation 502 34.11 Multiple Equivalent Solutions 504 34.12 Takeaway 504 34.13 Exercises 504 Section 6 Analysis of Leapfrogging Optimization 505 35 Analysis of Leapfrogging 507 35.1 Introduction 507 35.2 Balance in an Optimizer 508 35.3 Number of Initializations to be Confident That the Best Will Draw All Others to the Global Optimum 510 35.3.1 Methodology 511 35.3.2 Experimental 512 35.3.3 Results 513 35.4 Leap-To Window Amplification Analysis 515 35.5 Analysis of and M to Prevent Convergence on the Side of a Hill 519 35.6 Analysis of and M to Minimize NOFE 521 35.7 Probability Distribution of Leap-Overs 522 35.7.1 Data 526 35.8 Takeaway 527 35.9 Exercises 528 Section 7 Case Studies 529 36 Case Study 1: Economic Optimization of a Pipe System 531 36.1 Process and Analysis 531 36.1.1 Deterministic Continuum Model 531 36.1.2 Deterministic Discontinuous Model 534 36.1.3 Stochastic Discontinuous Model 535 36.2 Exercises 536 37 Case Study 2: Queuing Study 539 37.1 The Process and Analysis 539 37.2 Exercises 541 38 Case Study 3: Retirement Study 543 38.1 The Process and Analysis 543 38.2 Exercises 550 39 Case Study 4: A Goddard Rocket Study 551 39.1 The Process and Analysis 551 39.2 Pre-Assignment Note 554 39.3 Exercises 555 40 Case Study 5: Reservoir 557 40.1 The Process and Analysis 557 40.2 Exercises 559 41 Case Study 6: Area Coverage 561 41.1 Description and Analysis 561 41.2 Exercises 562 42 Case Study 7: Approximating Series Solution to an ODE 565 42.1 Concepts and Analysis 565 42.2 Exercises 568 43 Case Study 8: Horizontal Tank Vapor-Liquid Separator 571 43.1 Description and Analysis 571 43.2 Exercises 576 44 Case Study 9: In Vitro Fertilization 579 44.1 Description and Analysis 579 44.2 Exercises 583 45 Case Study 10: Data Reconciliation 585 45.1 Description and Analysis 585 45.2 Exercises 588 Section 8 Appendices 591 Appendix A Mathematical Concepts and Procedures 593 Appendix B Root Finding 605 Appendix C Gaussian Elimination 611 Appendix D Steady-State Identification in Noisy Signals 621 Appendix E Optimization Challenge Problems (2-D and Single OF) 635 Appendix F Brief on VBA Programming: Excel in Office 2013 709 Section 9 References and Index 717 References and Additional Resources 719 Index 723

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詳細情報

• NII書誌ID(NCID): BB26501170
• ISBN:
  • 9781118936337
• LCCN: 2017052555
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Hoboken, N.J.,[New York.]
• ページ数/冊数: xxxvii, 731 p.
• 大きさ: 25 cm
• 分類:
  • LCC : TA342
  • DC23 : 620.001/5196
• 件名:
  • LCSH : Engineering -- Mathematical models
  • LCSH : Mathematical optimization
• 親書誌ID:
  • BB25500208