‏821.00 ‏₪

Engineering Optimization: Theory and Practice 5th Edition

‏821.00 ‏₪

ISBN13

9781119454717

יצא לאור ב

Hoboken

מהדורה

5th Edition

עמודים / Pages

880

פורמט

Hardback

תאריך יציאה לאור

30 בדצמ׳ 2019

מחליף את פריט

14518352000

לדלג לסוף של גלריית תמונות

לדלג להתחלה של גלריית תמונות

פרטים

Engineering Optimization: Theory and Practice, Fifth Edition enables readers to quickly master and apply all the important optimization methods in use today across a broad range of industries. Covering both the latest and classical optimization methods, the text book starts off with the basics and then progressively builds to advanced principles and applications. This fifth edition has been updated to include four new chapters: Solution of Optimization Problems Using MATLAB; Metaheuristic Optimization Methods; Multi-Objective Optimization Methods; and Practical Implementation of Optimization. Each topic is written as a self-contained unit with all concepts explained fully and derivations presented. Computational aspects are emphasized throughout, with design examples and problems taken from different areas of engineering. This textbook includes solved examples, review questions and problems, and is accompanied by a website hosting a solutions manual.

מידע נוסף

מידע נוסף
מהדורה	5th Edition
עמודים / Pages	880
מחליף את פריט	14518352000
פורמט	Hardback
ISBN10	1119454719
יצא לאור ב	Hoboken
תאריך יציאה לאור	30 בדצמ׳ 2019
תוכן עניינים	Preface Acknowledgment About the Companion Website 1. Introduction to Optimization 1.1 Introduction 1.2 Historical Development 1.3 Engineering Applications of Optimization 1.4 Statement of an Optimization Problem 1.4.1 Design Vector 1.4.2 Design Constraints 1.4.3 Constraint Surface 1.4.4 Objective Function 1.4.5 Objective Function Surfaces 1.5 Classification of Optimization Problems 1.5.1 Classification Based on the Existence of Constraints 1.5.2 Classification Based on the Nature of the Design Variables 1.5.3 Classification Based on the Physical Structure of the Problem 1.5.4 Classification Based on the Nature of the Equations Involved 1.5.5 Classification Based on the Permissible Values of the Design Variables 1.5.6 Classification Based on the Deterministic Nature of the Variables 1.5.7 Classification Based on the Separability of the Functions 1.5.8 Classification Based on the Number of Objective Functions 1.6 Classification Based on the Number of Objective Functions 1.7 Engineering Optimization Literature References & Bibliography Review Questions Problems 2 Classical Optimization Techniques 2.1 Introduction 2.2 Single-Variable Optimization 2.3 Multivariable Optimization with No Constraints 2.3.1 Semidefinite Case 2.3.2 Saddle Point 2.4 Multivariable Optimization with Equality Constraints 2.4.1 Solution by Direct Substitution 2.4.2 Solution by the Method of Constrained Variation 2.4.3 Solution by the Method of Lagrange Multipliers 2.5 Multivariable Optimization with Inequality Constraints 2.5.1 Kuhn-Tucker Conditions 2.5.2 Constraint Qualification 2.6 Convex Programming Problem References and Bibliography Review Questions Problems 3. Linear Programming I: Simplex Method 3.1 Introduction 3.2 Applications of Linear Programming 3.3 Standard Form of a Linear Programming Problem 3.4 Geometry of Linear Programming Problems 3.5 Definitions and Theorems 3.6 Solution of a System of Linear Simultaneous Equations 3.7 Pivotal Reduction of a General System of Equations 3.8 Motivation of the Simplex Method 3.9 Simplex Algorithm 3.10 Two Phases of the Simplex Method References and Bibliography Review Questions Problems 4. Linear Programming II: Additional Topics and Extensions 4.1 Introduction 4.2 Revised Simplex Method 4.3 Duality in Linear Programming 4.3.1 Symmetric Primal-Dual Relations 4.3.2 General Primal-Dual Relations 4.3.3 Primal-Dual Relations When the Primal Is in Standard Form 4.3.4 Duality Theorems 4.3.5 Dual Simplex Method 4.4 Decomposition Principle 4.5 Sensitivity or Postoptimality Analysis 4.5.1 Changes in the Right-Hand-Side Constants bi 4.5.2 Changes in the Cost Coefficients cj 4.5.3 Addition of New Variables 4.5.4 Changes in the Constraint Coefficients aij 4.5.5 Addition of Constraints 4.6 Transportation Problem 4.7 Karmarkar's Interior Method 4.7.1 Statement of the Problem 4.7.2 Conversion of an LP Problem into the Required Form 4.7.3 Algorithm 4.8 Quadratic Programming Solutions Using Matlab References and Bibliography Review Questions Problems 5. Nonlinear Programming I: One-Dimensional Minimization Methods 5.1 Introduction 5.2 Unimodal Function 5.3 Unrestricted Search 5.4 Exhaustive Search 5.5 Dichotomous Search 5.6 Interval Halving Method 5.7 Fibonacci Method 5.8 Golden Section Method 5.9 Comparison of Elimination Methods 5.10 Quadratic Interpolation Method 5.11 Cubic Interpolation Method 5.12 Direct Root Methods 5.12.1 Newton Method 5.12.2 Quasi-Newton Method 5.12.3 Secant Method 5.13 Practical Considerations 5.13.1 How to Make the Methods Efficient and More Reliable 5.13.2 Implementation in Multivariable Optimization Problems 5.13.3 Comparison of Methods Solutions Using Matlab References and Bibliography Review Questions Problems 6. Nonlinear Programming II: Unconstrained Optimization Techniques 6.1 Introduction 6.1.1 Classification of Unconstrained Minimization Methods 6.1.2 General Approach 6.1.3 Rate of Convergence 6.1.4 Scaling of Design Variables 6.2 Random Search Methods 6.2.1 Random Jumping Method 6.2.2 Random Walk Method 6.2.3 Random Walk Method with Direction Exploitation 6.2.4 Advantages of Random Search Methods 6.3 Grid Search Method 6.4 Univariate Method 6.5 Pattern Directions 6.6 Powell's Method 6.6.1 Conjugate Directions 6.6.2 Algorithm 6.7 Simplex Method 6.7.1 Reflection 6.7.2 Expansion 6.7.3 Contraction 6.8 Gradient of a Function 6.8.1 Evaluation of the Gradient 6.8.2 Rate of Change of a Function along a Direction 6.9 Steepest Descent (Cauchy) Method 6.10 Conjugate Gradient (Fletcher-Reeves) Method 6.10.1 Development of the Fletcher-Reeves Method 6.10.2 Fletcher-Reeves Method 6.11 Newton's Method 6.12 Marquardt Method 6.13 Quasi-Newton Methods 6.13.1 Rank 1 Updates 6.14 Davidon-Fletcher-Powell Method 6.15 Broyden-Fletcher-Goldfarb-Shanno Method 6.16 Test Functions Solutions Using Matlab References and Bibliography Review Questions Problems 7. Nonlinear Programming III: Constrained Optimization Techniques 7.1 Introduction 7.2 Characteristics of a Constrained Problem 7.3 Random Search Methods 7.4 Complex Method 7.5 Sequential Linear Programming 7.6 Basic Approach in the Methods of Feasible Directions 7.7 Zoutendijk's Method of Feasible Directions 7.7.1 Direction-Finding Problem 7.7.2 Determination of Step Length 7.7.3 Termination Criteria 7.8 Rosen's Gradient Projection Method 7.8.1 Determination of Step Length 7.9 Generalized Reduced Gradient Method 7.10 Sequential Quadratic Programming 7.10.1 Derivation 7.10.2 Solution Procedure 7.11 Transformation Techniques 7.12 Basic Approach of the Penalty Function Method 7.13 Interior Penalty Function Method 7.14 Convex Programming Problem 7.15 Exterior Penalty Function Method 7.16 Extrapolation Techniques in the Interior Penalty Function Method 7.16.1 Extrapolation of the Design Vector X 7.16.2 Extrapolation of the Function f 7.17 Extended Interior Penalty Function Methods 17.1 Linear Extended Penalty Function Method 7.17.2 Quadratic Extended Penalty Function Method 7.18 Penalty Function Method for Problems with Mixed Equality and Inequality Constraints 7.18.1 Interior Penalty Function Method 7.18.2 Exterior Penalty Function Method 7.19 Penalty Function Method for Parametric Constraints 7.19.1 Parametric Constraint 7.19.2 Handling Parametric Constraints 7.20 Augmented Lagrange Multiplier Method 7.20.1 Equality-Constrained Problems 7.20.2 Inequality-Constrained Problems 7.20.3 Mixed Equality-Inequality-Constrained Problems 7.21 Checking the Convergence of Constrained Optimization Problems 7.21.1 Perturbing the Design Vector 7.21.2 Testing the Kuhn-Tucker Conditions 7.22 Test Problems 7.22.1 Design of a Three-Bar Truss 7.22.2 Design of a Twenty-Five-Bar Space Truss 7.22.3 Welded Beam Design 7.22.4 Speed Reducer (Gear Train) Design 7.22.5 Heat Exchanger Design [7.42] Solutions Using Matlab References and Bibliography Review Questions Problems 8. Geometric Programming 8.1 Introduction 8.2 Posynomial 8.3 Unconstrained Minimization Problem 8.4 Solution of an Unconstrained Geometric Programming Program Using Differential Calculus 8.5 Solution of an Unconstrained Geometric Programming Problem Using Arithmetic-Geometric Inequality 8.6 Primal-Dual Relationship and Sufficiency Conditions in the Unconstrained Case 8.7 Constrained Minimization 8.8 Solution of a Constrained Geometric Programming Problem 8.9 Primal and Dual Programs in the Case of Less-Than Inequalities 8.10 Geometric Programming with Mixed Inequality Constraints 8.11 Complementary Geometric Programming 8.12 Applications of Geometric Programming References and Bibliography 9. Dynamic Programming 9.1 Introduction 9.2 Multistage Decision Processes 9.2.1 Definition and Examples 9.2.2 Representation of a Multistage Decision Process 9.2.3 Conversion of a Nonserial System to a Serial System 9.2.4 Types of Multistage Decision Problems 9.3 Concept of Suboptimization and Principle of Optimality 9.4 Computational Procedure in Dynamic Programming 9.5 Example Illustrating the Calculus Method of Solution 9.6 Example Illustrating the Tabular Method of Solution 9.7 Conversion of a Final Value Problem into an Initial Value Problem 9.8 Linear Programming as a Case of Dynamic Programming 9.9 Continuous Dynamic Programming 9.10 Additional Applications 9.10.1 Design of Continuous Beams 9.10.2 Optimal Layout (Geometry) of a Truss 9.10.3 Optimal Design of a Gear Train 9.10.4 Design of a Minimum-Cost Drainage System References and Bibliography Review Questions Problems 10. Integer Programming 10.1 Introduction 10.2 Graphical Representation 10.3 Gomory's Cutting Plane Method 10.4 Balas' Algorithm for Zero-One Programming Problems 10.5 Integer Polynomial Programming 10.5.1 Representation of an Integer Variable by an Equivalent System of Binary Variables 10.5.2 Conversion of a Zero-One Polynomial Programming Problem into a Zero-One LP Problem 10.6 Branch-and-Bound Method 10.7 Sequential Linear Discrete Programming 10.8 Generalized Penalty Function Method Solutions Using Matlab References and Bibliography Review Questions Problems 11. Stochastic Programming 11.2 Basic Concepts of Probability Theory 11.2.1 Definition of Probability 11.2.2 Random Variables and Probability Density Functions 11.2.3 Mean and Standard Deviation 11.2.4 Function of a Random Variable 11.2.5 Jointly Distributed Random Variables 11.2.6 Covariance and Correlation 11.2.7 Functions of Several Random Variables 11.2.8 Probability Distributions 11.2.9 Central Limit Theorem 11.3 Stochastic Linear Programming 11.4 Stochastic Nonlinear Programming 11.4.1 Objective Function 11.4.2 Constraints 11.5 Stochastic Geometric Programming References and Bibliography Review Questions Problems 12. Optimal Control and Optimality Criteria Methods 12.1 Introduction 12.2 Calculus of Variations 12.2.1 Introduction 12.2.2 Problem of Calculus of Variations 12.2.3 Lagrange Multipliers and Constraints 12.3 Optimal Control Theory 12.3.1 Necessary Conditions for Optimal Control 12.3.2 Necessary Conditions for a General Problem 12.4 Optimality Criteria Methods 12.4.1 Optimality Criteria with a Single Displacement Constraint 12.4.2 Optimality Criteria with Multiple Displacement Constraints 12.4.3 Reciprocal Approximations References and Bibliography Review Questions Problems 13. Modern Methods of Optimization 13.1 Introduction 13.2 Genetic Algorithms 13.2.1 Introduction 13.2.2 Representation of Design Variables 13.2.3 Representation of Objective Function and Constraints 13.2.4 Genetic Operators 13.2.5 Algorithm 13.2.6 Numerical Results 13.3 Simulated Annealing 13.3.2 Procedure 13.3.3 Algorithm 13.3.4 Features of the Method 13.3.5 Numerical Results 13.4 Particle Swarm Optimization 13.4.1 Introduction 13.4.2 Computational Implementation of PSO 13.4.3 Improvement to the Particle Swarm Optimization Method 13.4.4 Solution of the Constrained Optimization Problem 13.5 Ant Colony Optimization 13.5.1 Basic Concept 13.5.2 Ant Searching Behavior 13.5.3 Path Retracing and Pheromone Updating 13.5.4 Pheromone Trail Evaporation 13.5.5 Algorithm 13.6 Optimization of Fuzzy Systems 13.6.1 Fuzzy Set Theory 13.6.2 Optimization of Fuzzy Systems 13.6.3 Computational Procedure 13.7 Neural-Network-Based Optimization References and Bibliography Review Questions Problems 14. Metaheuristic Optimization Methods 14.1 Definitions 14.2 Metaphors associated with metaheuristic optimization methods 14.3 Details of Representative Mataheuristic Algorithms 14.3.1 Crow search algorithm 14.3.2 Firefly Optimization Algorithm (FOA) 14.3.3 Harmony Search Algorithm 14.3.4 Teaching-Learning-Based Optimization (TLBO) 14.3.5 Honey Bee Swarm Optimization Algorithm References Review Questions 15. Practical Aspects of Optimization 15.1 Introduction 15.2 Reduction of Size of an Optimization Problem 15.2.1 Reduced Basis Technique 15.2.2 Design Variable Linking Technique 15.3 Fast Reanalysis Techniques 15.3.1 Incremental Response Approach 15.3.2 Basis Vector Approach 15.4 Derivatives of Static Displacements and Stresses 15.5 Derivatives of Eigenvalues and Eigenvectors 15.5.1 Derivatives of i 15.5.2 Derivatives of Yi 15.6 Derivatives of Transient Response 15.7 Sensitivity of Optimum Solution to Problem Parameters 15.7.1 Sensitivity Equations Using Kuhn-Tucker Conditions References Review Questions Problems 16. Multilevel and Multiobjective Optimization 16.1 Introduction 16.2 Multilevel Optimization 16.2.1 Basic Idea 16.2.1 Basic Idea 16.3 Parallel Processing 16.4 Multiobjective Optimization 16.4.1 Utility Function Method 16.4.2 Inverted Utility Function Method 16.4.3 Global Criterion Method 16.4.4 Bounded Objective Function Method 16.4.5 Lexicographic Method 16.4.6 Goal Programming Method 16.4.7 Goal Attainment Method 16.4.8 Game Theory Approach Solutions Using Matlab References and Bibliography Review Questions Problems 17. Solution of Optimization Problems Using MATLAB 17.1 Introduction 17.2 Solution of General Nonlinear Programming Problems 17.3 Solution of Linear Programming Problems 17.4 Solution of Lp Problems Using Interior Point Method 17.5 Solution of Quadratic Programming Problems 17.6 Solution of One-Dimensional Minimization Problems 17.7 Solution of Unconstrained Optimization Problems 17.8 Matlab Solution of Constrained Optimization Problems 17.9 Solution of Binary Programming Problems Using Matlab 17.10 Solution of Multiobjective Problems Using Matlab References Problems Index