Yang Engineering Optimization

An Introduction with Metaheuristic Applications
1. Auflage 2010
ISBN: 978-0-470-64041-8
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 376 Seiten, E-Book

An accessible introduction to metaheuristics and optimization,featuring powerful and modern algorithms for application acrossengineering and the sciences
From engineering and computer science to economics andmanagement science, optimization is a core component for problemsolving. Highlighting the latest developments that have evolved inrecent years, Engineering Optimization: An Introduction withMetaheuristic Applications outlines popular metaheuristicalgorithms and equips readers with the skills needed to apply thesetechniques to their own optimization problems. With insightfulexamples from various fields of study, the author highlights keyconcepts and techniques for the successful application ofcommonly-used metaheuristc algorithms, including simulatedannealing, particle swarm optimization, harmony search, and geneticalgorithms.
The author introduces all major metaheuristic algorithms andtheir applications in optimization through a presentation that isorganized into three succinct parts:
* Foundations of Optimization and Algorithmsprovides a brief introduction to the underlying nature ofoptimization and the common approaches to optimization problems,random number generation, the Monte Carlo method, and the Markovchain Monte Carlo method
* Metaheuristic Algorithms presents commonmetaheuristic algorithms in detail, including genetic algorithms,simulated annealing, ant algorithms, bee algorithms, particle swarmoptimization, firefly algorithms, and harmony search
* Applications outlines a wide range ofapplications that use metaheuristic algorithms to solve challengingoptimization problems with detailed implementation while alsointroducing various modifications used for multi-objectiveoptimization
Throughout the book, the author presents worked-out examples andreal-world applications that illustrate the modern relevance of thetopic. A detailed appendix features important and popularalgorithms using MATLAB® and Octave software packages, and arelated FTP site houses MATLAB code and programs for easyimplementation of the discussed techniques. In addition, referencesto the current literature enable readers to investigate individualalgorithms and methods in greater detail.
Engineering Optimization: An Introduction with MetaheuristicApplications is an excellent book for courses on optimizationand computer simulation at the upper-undergraduate and graduatelevels. It is also a valuable reference for researchers andpractitioners working in the fields of mathematics, engineering,computer science, operations research, and management science whouse metaheuristic algorithms to solve problems in their everydaywork.

Autoren/Hrsg.

Yang, Xin-She

Inhaltsverzeichnis

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 20th 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.
3.4 Vector and Matrix Norms.
3.5 Eigenvalues and Definiteness.
3.6 Linear and Afine Functions.
3.7 Gradient and Hessian Matrices.
3.8 Convexity.
Exercises.
4 Classic Optimization Methods I.
4.1 Unconstrained Optimization.
4.2 Gradient-Based Methods.
4.2.1 Newton's Method.
4.3 Constrained Optimization.
4.4 Linear Programming.
4.5 Simplex Method.
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.
5.3 Trust-Region Method.
5.4 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.
7.2 Variations with Constraints.
7.3 Variations for Multiple Variables.
7.4 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 fi.
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.
10.3 Lfievy Flights.
10.4 Markov Chain.
10.5 Markov Chain Monte Carlo.
10.6 Markov Chain and Optimisation.
Exercises.
PART II METAHEURISTIC ALGORITHMS.
11 Genetic Algorithms.
11.1 Introduction.
11.2 Genetic Algorithms.
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.
14.3 Applications.
Exercises.
15 Particle Swarm Optimization.
15.1 Swarm Intelligence.
15.2 PSO algorithms.
15.3 Accelerated PSO.
15.4 Implementation.
15.5 Constraints.
Exercises.
16 Harmony Search.
16.1 Music-Based Algorithms.
16.2 Harmony Search.
16.3 Implementation.
Exercises.
17 Firey Algorithm.
17.1 Behaviour of Fireies.
17.2 Firey-Inspired Algorithm.
17.3 Implementation.
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.1 Genetic Algorithms.
B.2 Simulated Annealing.
B.3 Particle Swarm Optimization.
B.4 Harmony Search.
B.5 Firey Algorithm.
B.6 Nonlinear Optimization.
B.6.1 Spring Design.
B.6.2 Pressure Vessel.
Appendix C: Glossary 283.
Appendix D: Problem Solutions 305.
References 333.
Index 343.

Über Autor(innen)

XIN-SHE YANG, PhD, is Senior Research Fellow in the Department of Engineering at Cambridge University (United Kingdom). The Editor-in-Chief of International Journal of Mathematical Modeling and Numerical Optimization (IJMMNO), Dr. Yang has published more than sixty journal articles in his areas of research interest, which include computational mathematics, metaheuristic algorithms, numerical analysis, and engineering optimization.