Engineering Optimization

Xin-She Yang 지음

Wiley 2010년 07월 20일 출간

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An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciencesFrom engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms.The author introduces all major metaheuristic algorithms and their applications in optimization through a presentation that is organized into three succinct parts:Foundations of Optimization and Algorithms provides a brief introduction to the underlying nature of optimization and the common approaches to optimization problems, random number generation, the Monte Carlo method, and the Markov chain Monte Carlo methodMetaheuristic Algorithms presents common metaheuristic algorithms in detail, including genetic algorithms, simulated annealing, ant algorithms, bee algorithms, particle swarm optimization, firefly algorithms, and harmony searchApplications outlines a wide range of applications that use metaheuristic algorithms to solve challenging optimization problems with detailed implementation while also introducing various modifications used for multi-objective optimizationThroughout the book, the author presents worked-out examples and real-world applications that illustrate the modern relevance of the topic. A detailed appendix features important an

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List of Figures.Preface.Acknowledgments.Introduction.PART I Foundations of Optimization and Algorithms.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.3.4 Vector and Matrix Norms.3.5 Eigenvalues and Definiteness.3.6 Linear and Affine 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.4.3 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 p.9.2 Monte Carlo Integration.9.3 Importance of Sampling.Exercises.10 Random Walk and Ma

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저자(글) Xin-She Yang

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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.