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Engineering optimization : an introduction with metaheuristic applications / Xin-She Yang

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Hoboken, N.J : John Wiley, c2010Edition: Online-AusgDescription: Online-Ressource (1 online resource (xxvii, 347 p.)) : illISBN:
  • 9781282707771
  • 1282707779
  • 9780470640418
Subject(s): Additional physical formats: 9780470582466 | 0470582464 | 9780470640418 | Erscheint auch als: Engineering optimization. Druck-Ausgabe Hoboken, N.J : Wiley, 2010. XXVII, 347 SDDC classification:
  • 620.0015196
  • 620.001/5196
RVK: RVK: SK 970LOC classification:
  • T57.84
Online resources: Summary: 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 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 method Metaheuristic Algorithms presents common metaheuristic algorithms in detail, including genetic algorithms, simulated annealing, ant algorithms, bee algorithms, particle swarm optimization, firefly algorithms, and harmony search Applications 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 optimization Throughout the book, the author presents worked-out examples and real-world applications that illustrate the modern relevance of theSummary: 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.PPN: PPN: 80731367XPackage identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PAD | ZDB-30-PQE
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