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Informationen zum Autor EL-GHAZALI TALBI , PHD, is Professor in the Computer Science Laboratory of the University of Lille, France. His research interests include parallel algorithms for combinatorial optimization and their applications to generic and real-world problems. Dr. Talbi leads the OPAC (Parallel Cooperative Optimization) research team; is the scientific leader of the INRIA DOLPHIN project dealing with distributed multi-objective optimization; and is active in several research and industrial projects, publications, and international conferences in the field. Klappentext Learn to solve complex problems with efficient parallel optimization algorithmsThis text provides an excellent balance of theory and application that enables readers to deploy powerful algorithms, frameworks, and methodologies to solve complex optimization problems in a diverse range of industries. Each chapter is written by leading experts in the fields of parallel and distributed optimization. Collectively, the contributions serve as a complete reference to the field of combinatorial optimization, including details and findings of recent and ongoing investigations.Readers learn to solve large-scale problems quickly and efficiently with the text's clear coverage of several parallel optimization algorithms:* Exact algorithms, including branch and bound, dynamic programming, branch and cut, semidefinite programming, and constraint programming* Metaheuristics, including local search, tabu search, simulated annealing, scatter search, GRASP, variable neighborhood search, ant colonies, genetic programming, evolution strategies, and genetic algorithms* Hybrid approaches, combining exact algorithms and metaheuristics* Multi-objective optimization algorithmsThe text not only presents parallel algorithms and applications, but also software frameworks and libraries that integrate parallel algorithms for combinatorial optimization. Among the well-known parallel and distributed frameworks covered are COIN, ParadisEO, BOB++, MW, and SDPARA.Numerous real-world examples of problems and solutions demonstrate how parallel combinatorial optimization is applied in such fields as telecommunications, logistics, genomics, networking, and transportation. Whether you are a practicing engineer, field researcher, or student, this text provides you with not only the theory of parallel combinatorial optimization, but the guidance and practical tools to solve complex problems using powerful algorithms. Zusammenfassung Learn to solve complex problems with efficient parallel optimization algorithmsThis text provides an excellent balance of theory and application that enables readers to deploy powerful algorithms, frameworks, and methodologies to solve complex optimization problems in a diverse range of industries. Each chapter is written by leading experts in the fields of parallel and distributed optimization. Collectively, the contributions serve as a complete reference to the field of combinatorial optimization, including details and findings of recent and ongoing investigations.Readers learn to solve large-scale problems quickly and efficiently with the text's clear coverage of several parallel optimization algorithms:* Exact algorithms, including branch and bound, dynamic programming, branch and cut, semidefinite programming, and constraint programming* Metaheuristics, including local search, tabu search, simulated annealing, scatter search, GRASP, variable neighborhood search, ant colonies, genetic programming, evolution strategies, and genetic algorithms* Hybrid approaches, combining exact algorithms and metaheuristics* Multi-objective optimization algorithmsThe text not only presents parallel algorithms and applications, but also software frameworks and libraries that integrate parallel algorithms for combinatorial optimization. Among the well-known parallel and distributed frameworks covered are COIN, ParadisEO, BO...
List of contents
Preface.
Acknowledgments.
Contributors.
1. Parallel Branch-and-Bound Algorithms (T. Crainic, B. Lecun, C. Roucairol).
2. Parallel Dynamic Programming (F. Almeida, D. Gonzalez, I. Pelaez).
3. Parallel Branch and Cut (T. Ralphs).
4. Parallel Semidefinite Programming and Combinatorial Optimization (S. J. Benson).
5. Parallel Resolution of the Satisfiability Problem: A Survey (D. Singer).
6. Parallel Metaheuristics: Algorithms and Frameworks (N. Melab, E-G. Talbi, S. Cahon, E. Alba, G. Luque).
7. Towards Parallel Design of Hybrids between Metaheuristics and Exact Methods (M. Basseur, L. Jourdan, E-G. Talbi).
8. Parallel Exact Methods for Multiobjective Combinatorial Optimization (C. Dhaenens, J. Lemesre, N. Melab, M. Mezmaz, E-G. Talbi).
9. Parallel Primal-Dual Interior Point Methods for Semidefinite Programs (M. Yamashita, K. Fujisawa, M. Fukuda, M. Kojima, K. Nakata).
10. MW: A Software Framework for Combinatorial Optimization on Computational Grids (W. Glankwamdee, T. Linderoth).
11. Constraint Logic Programming on Multiple Processors (I. Sakellariou, I. Vlahavas).
12. Application of Parallel Metaheuristics to Optimization Problems in Telecommunications and Bioinformatics (S. L. Martins, C. Ribeiro, I. Rosseti).
Index.
Report
"The target audience will learn a lot from the book, and I hope they will be inspired..." ( Computing Reviews.com , May 30, 2007)
