| 036a | XA-DE |
| 037b | eng |
| 077a | 371944198 Buchausg. u.d.T.: ‡Handbook of optimization |
| 087q | 978-3-642-30503-0 |
| 100 | Zelinka, Ivan |
| 104b | Snášel, Václav |
| 108b | Abraham, Ajith |
| 331 | Handbook of Optimization |
| 335 | From Classical to Modern Approach |
| 410 | Berlin ; Heidelberg |
| 412 | Springer |
| 425 | 2013 |
| 425a | 2013 |
| 433 | Online-Ressource (XII, 1100 p. 460 illus, digital) |
| 451 | Intelligent Systems Reference Library ; 38 |
| 501 | Description based upon print version of record |
| 517 | Title; Preface; Contents; ClassicalMethods - Theory; ClassicalMethods - Applications; Heuristics - Theory; Heuristics - Applications; Dynamic Optimization Using Analytic and Evolutionary Approaches: A ComparativeReview; Introduction; Optimal Control and Dynamic Fitness Landscapes; Optimal Control; The Linear Quadratic Control Problem and Its Dynamic Fitness Landscape; Solutions for the General Optimal Control Problem; Evolutionary Computation and Dynamic Fitness Landscapes; Evolutionary Approaches to Dynamic Optimization; Detecting and Reacting to Change; Conclusions; References. Bounded Dual Simplex Algorithm: Definition and StructureBounded Dual Simplex Algorithm; Definitions and Concepts; Chapter Organization; Structure of the Bounded Dual Simplex Algorithm; Bounded Dual Simplex Method; Bounded Dual Simplex in Tableau Format; Algorithm; Bounded Dual Simplex Algorithm with Re-optimization; Application of the Bounded Dual Simplex Algorithm in Electrical Engineering; Transmission Network Expansion Planning Problem; Illustrative Example; Conclusions; References; Some Results on Subanalytic Variational Inclusions; Introduction; Preliminary Results; Divided Differences. Semianalytic and Subanalytic Sets and FunctionsPseudo-Lipschitz Maps; ANewton-TypeMethod; Description of the Method and Assumptions; Convergence Results; The Study of Perturbed Problems; An Iterative Method in the Lipschitz Case; A Secant-Type Method; Conclusion; References; Graph and Geometric Algorithms and Efficient Data Structures; Introduction; Basic Notions; Minimum Spanning Tree Problem; Minimum Network Steiner Tree Problem; Distance Network Approximation (DNA); Euclidean Minimum Spanning Tree Problem; Euclidean Steiner Tree Problem; Robot Motion Planning; Cell Decomposition. Roadmap MethodsConclusions; References; An Exact Algorithm for the Continuous Quadratic Knapsack Problem via Infimal Convolution; Introduction; Theoretical Part; Previous results; Algorithm; The Continuous Quadratic Knapsack Problem; Computational Complexity; Application Part; Economic Dispatch Problem; Large-Scale QP; Computer Code; Conclusions; References; Game Theoretic and Bio-inspired Optimization Approach for Autonomous Movement of MANET Nodes; Introduction; Related Work; Brief Literature Review; Our Previous Work; Background to GT and GA; Game Theory; Genetic Algorithms. Our Node Spreading Bio-inspired GameFinding Next Preferred Locations Using FGA; Our Spatial Game; BioGame Implementation; Simulation Experiments; The Network Area Coverage; The Average Distance Traveled; Simulation of Hostile Attack and Random Node Malfunction; Conclusion; References; Multilocal Programming and Applications; Introduction; Bound Constrained Multilocal Programming; Stochastic Methods; Deterministic Methods; Numerical Experiments; Constrained Multilocal Programming; The Penalty Function Method; Numerical Experiments; Engineering Applications; Phase Stability. Numerical Experiments |
| 527 | Buchausg. u.d.T.: ‡Handbook of optimization |
| 540a | ISBN 978-3-642-30504-7 |
| 700 | |UYQ |
| 700 | |COM004000 |
| 700 | |*90-00 |
| 700 | |90-06 |
| 700 | |90C90 |
| 700b | |006.3 |
| 700c | |Q342 |
| 700g | 127088123X SK 870 |
| 750 | Optimization problems were and still are the focus of mathematics from antiquity to the present. Since the beginning of our civilization, the human race has had to confront numerous technological challenges, such as finding the optimal solution of various problems including control technologies, power sources construction, applications in economy, mechanical engineering and energy distribution amongst others. These examples encompass both ancient as well as modern technologies like the first electrical energy distribution network in USA etc. Some of the key principles formulated in the middle ages were done by Johannes Kepler (Problem of the wine barrels), Johan Bernoulli (brachystochrone problem), Leonhard Euler (Calculus of Variations), Lagrange (Principle multipliers), that were formulated primarily in the ancient world and are of a geometric nature. In the beginning of the modern era, works of L.V. Kantorovich and G.B. Dantzig (so-called linear programming) can be considered amongst others. This book discusses a wide spectrum of optimization methods from classical to modern, alike heuristics. Novel as well as classical techniques is also discussed in this book, including its mutual intersection. Together with many interesting chapters, a reader will also encounter various methods used for proposed optimization approaches, such as game theory and evolutionary algorithms or modelling of evolutionary algorithm dynamics like complex networks |
| 753 | Optimization problems were and still are the focus of mathematics from antiquity to the present. Since the beginning of our civilization, the human race has had to confront numerous technological challenges, such as finding the optimal solution of various problems including control technologies, power sources construction, applications in economy, mechanical engineering and energy distribution amongst others. These examples encompass both ancient as well as modern technologies like the first electrical energy distribution network in USA etc. Some of the key principles formulated in the middle ages were done by Johannes Kepler (Problem of the wine barrels), Johan Bernoulli (brachystochrone problem), Leonhard Euler (Calculus of Variations), Lagrange (Principle multipliers), that were formulated primarily in the ancient world and are of a geometric nature. In the beginning of the modern era, works of L.V. Kantorovich and G.B. Dantzig (so-called linear programming) can be considered amongst others. This book discusses a wide spectrum of optimization methods from classical to modern, alike heuristics. Novel as well as classical techniques is also discussed in this book, including its mutual intersection. Together with many interesting chapters, a reader will also encounter various methods used for proposed optimization approaches, such as game theory and evolutionary algorithms or modelling of evolutionary algorithm dynamics like complex networks. |
| 902s | 209057009 Optimierung |
| 012 | 373500998 |
| 081 | Zelinka, Ivan: Handbook of Optimization |
| 100 | Springer E-Book |
| 125a | Elektronischer Volltext - Campuslizenz |
| 655e | $uhttp://dx.doi.org/10.1007/978-3-642-30504-7 |
