Metaheuristic optimization via memory and evolution : tabu search and scatter search
Tabu Search (TS) and, more recently, Scatter Search (SS) have proved highly effective in solving a wide range of optimization problems, and have had a variety of applications in industry, science, and government. The goal of METAHEURISTIC OPTIMIZATION VIA MEMORY AND EVOLUTION: Tabu Search and Scatte...
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| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
New York, NY :
Springer US
2005.
Cham : Springer Nature |
| Edice: | Operations Research/Computer Science Interfaces Series
30 |
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| Edition sous un autre format: | • Metaheuristic Optimization via Memory and Evolution, Texte imprimé, 9781402081347 • Metaheuristic Optimization via Memory and Evolution, Texte imprimé, 9781441954831 • Metaheuristic Optimization via Memory and Evolution, Texte imprimé, 9780387522661 |
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| 041 | 0 | |a eng | |
| 082 | |a 519.6 | ||
| 100 | 1 | |a Rego, Cesar. | |
| 245 | 1 | 0 | |a Metaheuristic optimization via memory and evolution : |b tabu search and scatter search |c edited by Ramesh Sharda, Stefan Voß, César Rego, Bahram Alidaee. |
| 260 | |a New York, NY : |b Springer US. | ||
| 260 | |a Cham : |b Springer Nature, |c 2005. | ||
| 490 | 0 | |a Operations Research/Computer Science Interfaces Series |v 30 |x 2698-5489 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 0 | |a Advances for New Model and Solution Approaches -- A Scatter Search Tutorial for Graph-Based Permutation Problems -- A Multistart Scatter Search Heuristic for Smooth NLP and MINLP Problems -- Scatter Search Methods for the Covering Tour Problem -- Solution of the SONET Ring Assignment Problem with Capacity Constraints -- Advances for Solving Classical Problems -- A Very Fast Tabu Search Algorithm for Job Shop Problem -- Tabu Search Heuristics for the Vehicle Routing Problem -- Some New Ideas in TS for Job Shop Scheduling -- A Tabu Search Heuristic for the Uncapacitated Facility Location Problem -- Adaptive Memory Search Guidance for Satisfiability Problems -- Experimental Evaluations -- Lessons from Applying and Experimenting with Scatter Search -- Tabu Search for Mixed Integer Programming -- Scatter Search vs. Genetic Algorithms -- Review of Recent Developments -- Parallel Computation, Co-operation, Tabu Search -- Using Group Theory to Construct and Characterize Metaheuristic Search Neighborhoods -- Logistics Management -- New Procedural Designs -- On the Integration of Metaheuristic Strategies in Constraint Programming -- General Purpose Metrics for Solution Variety -- Controlled Pool Maintenance for Metaheuristics -- Adaptive Memory Projection Methods for Integer Programming -- RAMP: A New Metaheuristic Framework for Combinatorial Optimization. | |
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| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a Tabu Search (TS) and, more recently, Scatter Search (SS) have proved highly effective in solving a wide range of optimization problems, and have had a variety of applications in industry, science, and government. The goal of METAHEURISTIC OPTIMIZATION VIA MEMORY AND EVOLUTION: Tabu Search and Scatter Search is to report original research on algorithms and applications of tabu search, scatter search or both, as well as variations and extensions having "adaptive memory programming" as a primary focus. Individual chapters identify useful new implementations or new ways to integrate and apply the principles of TS and SS, or that prove new theoretical results, or describe the successful application of these methods to real world problems. From the preface: Where Are We Headed? The chapters of this book provide a series of landmarks along the way as we investigate and seek to better understand the elements of tabu search and scatter search that account for their successes in an astonishingly varied range of applications. The contributions of the chapters are diverse in scope, and are not uniform in the degree that they plumb or take advantage of fundamental principles underlying TS and SS. Collectively, however, they offer a useful glimpse of issues that deserve to be set in sharper perspective, and that move us farther along the way toward dealing with problems whose size and complexity pose key challenges to the optimization methods of tomorrow... Fred Glover University of Colorado. | ||
| 700 | 1 | |a Alidaee, Bahram. |4 pbd | |
| 776 | 0 | |t Metaheuristic Optimization via Memory and Evolution |b Texte imprimé |z 9781402081347 | |
| 776 | 0 | |t Metaheuristic Optimization via Memory and Evolution |b Texte imprimé |z 9781441954831 | |
| 776 | 0 | |t Metaheuristic Optimization via Memory and Evolution |b Texte imprimé |z 9780387522661 | |
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