Robust Optimization and Sensitivity Analysis with Multi-objective Genetic Algorithms
Author: Mian Li
Publisher:
Published: 2007
Total Pages:
ISBN-13:
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Author: Mian Li
Publisher:
Published: 2007
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Mitsuo Gen
Publisher: John Wiley & Sons
Published: 1999-12-28
Total Pages: 520
ISBN-13: 9780471315315
DOWNLOAD EBOOKIm Mittelpunkt dieses Buches steht eines der wichtigsten Optimierungsverfahren der industriellen Ingenieurtechnik: Mit Hilfe genetischer Algorithmen lassen sich Qualität, Design und Zuverlässigkeit von Produkten entscheidend verbessern. Das Verfahren beruht auf der Wahrscheinlichkeitstheorie und lehnt sich an die Prinzipien der biologischen Vererbung an: Die Eigenschaften des Produkts werden, unter Beachtung der äußeren Randbedingungen, schrittweise optimiert. Ein hochaktueller Band international anerkannter Autoren. (03/00)
Author: Masatoshi Sakawa
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 294
ISBN-13: 146151519X
DOWNLOAD EBOOKSince the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a wide range of actual real world applications. The theoretical material and applications place special stress on interactive decision-making aspects of fuzzy multiobjective optimization for human-centered systems in most realistic situations when dealing with fuzziness. The intended readers of this book are senior undergraduate students, graduate students, researchers, and practitioners in the fields of operations research, computer science, industrial engineering, management science, systems engineering, and other engineering disciplines that deal with the subjects of multiobjective programming for discrete or other hard optimization problems under fuzziness. Real world research applications are used throughout the book to illustrate the presentation. These applications are drawn from complex problems. Examples include flexible scheduling in a machine center, operation planning of district heating and cooling plants, and coal purchase planning in an actual electric power plant.
Author: K.S. Tang
Publisher: CRC Press
Published: 2018-09-03
Total Pages: 283
ISBN-13: 1351832522
DOWNLOAD EBOOKThe first book to focus on jumping genes outside bioscience and medicine, Multiobjective Optimization Methodology: A Jumping Gene Approach introduces jumping gene algorithms designed to supply adequate, viable solutions to multiobjective problems quickly and with low computational cost. Better Convergence and a Wider Spread of Nondominated Solutions The book begins with a thorough review of state-of-the-art multiobjective optimization techniques. For readers who may not be familiar with the bioscience behind the jumping gene, it then outlines the basic biological gene transposition process and explains the translation of the copy-and-paste and cut-and-paste operations into a computable language. To justify the scientific standing of the jumping genes algorithms, the book provides rigorous mathematical derivations of the jumping genes operations based on schema theory. It also discusses a number of convergence and diversity performance metrics for measuring the usefulness of the algorithms. Practical Applications of Jumping Gene Algorithms Three practical engineering applications showcase the effectiveness of the jumping gene algorithms in terms of the crucial trade-off between convergence and diversity. The examples deal with the placement of radio-to-fiber repeaters in wireless local-loop systems, the management of resources in WCDMA systems, and the placement of base stations in wireless local-area networks. Offering insight into multiobjective optimization, the authors show how jumping gene algorithms are a useful addition to existing evolutionary algorithms, particularly to obtain quick convergence solutions and solutions to outliers.
Author: K. KrishnaKumar
Publisher:
Published: 1993
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Kalyanmoy Deb
Publisher: John Wiley & Sons
Published: 2001-07-05
Total Pages: 540
ISBN-13: 9780471873396
DOWNLOAD EBOOKOptimierung mit mehreren Zielen, evolutionäre Algorithmen: Dieses Buch wendet sich vorrangig an Einsteiger, denn es werden kaum Vorkenntnisse vorausgesetzt. Geboten werden alle notwendigen Grundlagen, um die Theorie auf Probleme der Ingenieurtechnik, der Vorhersage und der Planung anzuwenden. Der Autor gibt auch einen Ausblick auf Forschungsaufgaben der Zukunft.
Author: Sam Le-Corre
Publisher:
Published: 2019
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Jiyoung Im
Publisher:
Published: 2018
Total Pages: 97
ISBN-13:
DOWNLOAD EBOOKIn this thesis, we study the special case of linear optimization to show what may affect the sensitivity of the optimal value function under data uncertainty. In this special case, we show that the robust optimization problem with a locally smaller feasible region yields a more conservative robust optimal value than the one with a locally bigger feasible region. To achieve that goal, we use a geometric approach to analyze the sensitivity of the optimal value function for linear programming (LP) under data uncertainty. We construct a family of proper cones where the strict containment holds for any pair of cones in the family. We then form a family of LP problems using this family of cones constructed above; the feasible regions of each pair of LPs in the family holds strict containment, every LP in the family has the unique optimal solution at the vertex of the cone and has the same objective function, i.e., every LP in the family shares the same optimal solution and the same optimal value. We rewrite he LPs so that they reflect the given data uncertainty and perform local analysis near the optimal solutions where the local strict containment holds. Finally, we illustrate that an LP with a locally smaller feasible region is more sensitive than an LP with a locally bigger feasible region.
Author: National Aeronautics and Space Adm Nasa
Publisher:
Published: 2018-09-19
Total Pages: 46
ISBN-13: 9781723834097
DOWNLOAD EBOOKA genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.Holst, Terry L. and Pulliam, Thomas H.Ames Research CenterGENETIC ALGORITHMS; MATHEMATICAL MODELS; OPTIMIZATION; OPERATORS (MATHEMATICS); CONVERGENCE; VECTOR ANALYSIS; STOCHASTIC PROCESSES...
Author: Enrique Alba
Publisher: Springer
Published: 2008-06-03
Total Pages: 248
ISBN-13: 9780387776095
DOWNLOAD EBOOKCellular Genetic Algorithms defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book with equal and parallel emphasis on both theory and practice. This book is a key source for studying and designing cellular GAs, as well as a self-contained primary reference book for these algorithms.