Engineering optimization an introduction with metaheuristic applications.
An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting th...
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| Hlavní autor: | |
|---|---|
| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
Hoboken, N.J
WILEY
2010
Wiley John Wiley & Sons, Incorporated Wiley-Blackwell |
| Vydání: | 1 |
| Témata: | |
| ISBN: | 0470640413, 0470582464, 9780470640418, 9780470582466, 9780470640425, 0470640421 |
| On-line přístup: | Získat plný text |
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- Engineering optimization: an introduction with metaheuristic applications -- CONTENTS -- LIST OF FIGURES -- PREFACE -- ACKNOWLEDGMENTS -- INTRODUCTION -- PART I: FOUNDATIONS OF OPTIMIZATION AND ALGORITHMS -- CHAPTER 1: A BRIEF HISTORY OF OPTIMIZATION -- CHAPTER 2: ENGINEERING OPTIMIZATION -- CHAPTER 3: MATHEMATICAL FOUNDATIONS -- CHAPTER 4: CLASSIC OPTIMIZATION METHODS I -- CHAPTER 5: CLASSIC OPTIMIZATION METHODS II -- CHAPTER 6: CONVEX OPTIMIZATION -- CHAPTER 7: CALCULUS OF VARIATIONS -- CHAPTER 8: RANDOM NUMBER GENERATORS -- CHAPTER 9: MONTE CARLO METHODS -- CHAPTER 10: RANDOM WALK AND MARKOV CHAIN -- PART II: METAHEURISTIC ALGORITHMS -- CHAPTER 11: GENETIC ALGORITHMS -- CHAPTER 12: SIMULATED ANNEALING -- CHAPTER 13: ANT ALGORITHMS -- CHAPTER 14: BEE ALGORITHMS -- CHAPTER 15: PARTICLE SWARM OPTIMIZATION -- CHAPTER 16: HARMONY SEARCH -- CHAPTER 17: FIREFLY ALGORITHM -- PART III: APPLICATIONS -- CHAPTER 18: MULTIOBJECTIVE OPTIMIZATION -- CHAPTER 19: ENGINEERING APPLICATIONS -- APPENDIX A: TEST PROBLEMS IN OPTIMIZATION -- APPENDIX B: MATLAB® PROGRAMS -- APPENDIX C: GLOSSARY -- APPENDIX D: PROBLEM SOLUTIONS -- REFERENCES -- INDEX
- 5.4 Sequential Quadratic Programming -- 5.4.1 Quadratic Programming -- 5.4.2 Sequential Quadratic Programming -- Exercises -- 6 Convex Optimization -- 6.1 KKT Conditions -- 6.2 Convex Optimization Examples -- 6.3 Equality Constrained Optimization -- 6.4 Barrier Functions -- 6.5 Interior-Point Methods -- 6.6 Stochastic and Robust Optimization -- Exercises -- 7 Calculus of Variations -- 7.1 Euler-Lagrange Equation -- 7.1.1 Curvature -- 7.1.2 Euler-Lagrange Equation -- 7.2 Variations with Constraints -- 7.3 Variations for Multiple Variables -- 7.4 Optimal Control -- 7.4.1 Control Problem -- 7.4.2 Pontryagin's Principle -- 7.4.3 Multiple Controls -- 7.4.4 Stochastic Optimal Control -- Exercises -- 8 Random Number Generators -- 8.1 Linear Congruential Algorithms -- 8.2 Uniform Distribution -- 8.3 Other Distributions -- 8.4 Metropolis Algorithms -- Exercises -- 9 Monte Carlo Methods -- 9.1 Estimating π -- 9.2 Monte Carlo Integration -- 9.3 Importance of Sampling -- Exercises -- 10 Random Walk and Markov Chain -- 10.1 Random Process -- 10.2 Random Walk -- 10.2.1 ID Random Walk -- 10.2.2 Random Walk in Higher Dimensions -- 10.3 Lévy Flights -- 10.4 Markov Chain -- 10.5 Markov Chain Monte Carlo -- 10.5.1 Metropolis-Hastings Algorithms -- 10.5.2 Random Walk -- 10.6 Markov Chain and Optimisation -- Exercises -- PART II METAHEURISTIC ALGORITHMS -- 11 Genetic Algorithms -- 11.1 Introduction -- 11.2 Genetic Algorithms -- 11.2.1 Basic Procedure -- 11.2.2 Choice of Parameters -- 11.3 Implementation -- Exercises -- 12 Simulated Annealing -- 12.1 Annealing and Probability -- 12.2 Choice of Parameters -- 12.3 SA Algorithm -- 12.4 Implementation -- Exercises -- 13 Ant Algorithms -- 13.1 Behaviour of Ants -- 13.2 Ant Colony Optimization -- 13.3 Double Bridge Problem -- 13.4 Virtual Ant Algorithm -- Exercises -- 14 Bee Algorithms -- 14.1 Behavior of Honey Bees
- 14.2 Bee Algorithms -- 14.2.1 Honey Bee Algorithm -- 14.2.2 Virtual Bee Algorithm -- 14.2.3 Artificial Bee Colony Optimization -- 14.3 Applications -- Exercises -- 15 Particle Swarm Optimization -- 15.1 Swarm Intelligence -- 15.2 PSO algorithms -- 15.3 Accelerated PSO -- 15.4 Implementation -- 15.4.1 Multimodal Functions -- 15.4.2 Validation -- 15.5 Constraints -- Exercises -- 16 Harmony Search -- 16.1 Music-Based Algorithms -- 16.2 Harmony Search -- 16.3 Implementation -- Exercises -- 17 Firefly Algorithm -- 17.1 Behaviour of Fireflies -- 17.2 Firefly-Inspired Algorithm -- 17.2.1 Firefly Algorithm -- 17.2.2 Light Intensity and Attractiveness -- 17.2.3 Scaling and Global Optima -- 17.2.4 Two Special Cases -- 17.3 Implementation -- 17.3.1 Multiple Global Optima -- 17.3.2 Multimodal Functions -- 17.3.3 FA Variants -- Exercises -- PART III APPLICATIONS -- 18 Multiobjective Optimization -- 18.1 Pareto Optimality -- 18.2 Weighted Sum Method -- 18.3 Utility Method -- 18.4 Metaheuristic Search -- 18.5 Other Algorithms -- Exercises -- 19 Engineering Applications -- 19.1 Spring Design -- 19.2 Pressure Vessel -- 19.3 Shape Optimization -- 19.4 Optimization of Eigenvalues and Frequencies -- 19.5 Inverse Finite Element Analysis -- Exercises -- Appendices -- Appendix A: Test Problems in Optimization -- Appendix B: Matlab® Programs -- B.l Genetic Algorithms -- B.2 Simulated Annealing -- B.3 Particle Swarm Optimization -- B.4 Harmony Search -- B.5 Firefly Algorithm -- B.6 Large Sparse Linear Systems -- B.7 Nonlinear Optimization -- B.7.1 Spring Design -- B.7.2 Pressure Vessel -- Appendix C: Glossary -- Appendix D: Problem Solutions -- References -- Index
- Intro -- Engineering Optimization: An Introduction with Metaheuristic Applications -- CONTENTS -- List of Figures -- Preface -- Acknowledgments -- Introduction -- PART I FOUNDATIONS OF OPTIMIZATION AND ALGORITHMS -- 1 A Brief History of Optimization -- 1.1 Before 1900 -- 1.2 Twentieth Century -- 1.3 Heuristics and Metaheuristics -- Exercises -- 2 Engineering Optimization -- 2.1 Optimization -- 2.2 Type of Optimization -- 2.3 Optimization Algorithms -- 2.4 Metaheuristics -- 2.5 Order Notation -- 2.6 Algorithm Complexity -- 2.7 No Free Lunch Theorems -- Exercises -- 3 Mathematical Foundations -- 3.1 Upper and Lower Bounds -- 3.2 Basic Calculus -- 3.3 Optimality -- 3.3.1 Continuity and Smoothness -- 3.3.2 Stationary Points -- 3.3.3 Optimality Criteria -- 3.4 Vector and Matrix Norms -- 3.5 Eigenvalues and Definiteness -- 3.5.1 Eigenvalues -- 3.5.2 Definiteness -- 3.6 Linear and Affine Functions -- 3.6.1 Linear Functions -- 3.6.2 Affine Functions -- 3.6.3 Quadratic Form -- 3.7 Gradient and Hessian Matrices -- 3.7.1 Gradient -- 3.7.2 Hessian -- 3.7.3 Function approximations -- 3.7.4 Optimality of multivariate functions -- 3.8 Convexity -- 3.8.1 Convex Set -- 3.8.2 Convex Functions -- Exercises -- 4 Classic Optimization Methods I -- 4.1 Unconstrained Optimization -- 4.2 Gradient-Based Methods -- 4.2.1 Newton's Method -- 4.2.2 Steepest Descent Method -- 4.2.3 Line Search -- 4.2.4 Conjugate Gradient Method -- 4.3 Constrained Optimization -- 4.4 Linear Programming -- 4.5 Simplex Method -- 4.5.1 Basic Procedure -- 4.5.2 Augmented Form -- 4.6 Nonlinear Optimization -- 4.7 Penalty Method -- 4.8 Lagrange Multipliers -- 4.9 Karush-Kuhn-Tucker Conditions -- Exercises -- 5 Classic Optimization Methods II -- 5.1 BFGS Method -- 5.2 Nelder-Mead Method -- 5.2.1 A Simplex -- 5.2.2 Nelder-Mead Downhill Simplex -- 5.3 Trust-Region Method