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Author Taillard, Éric D. author. Author. http://id.loc.gov/vocabulary/relators/aut

Title Design of Heuristic Algorithms for Hard Optimization : With Python Codes for the Travelling Salesman Problem / by Éric D. Taillard.

Publication Info. Cham Springer Nature 2023.
Cham : Springer International Publishing : Imprint: Springer, 2023.

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Location Call No. Status
 University of Saint Joseph: Pope Pius XII Library - Internet  WORLD WIDE WEB E-BOOK Springer    Downloadable
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Edition 1st ed. 2023.
Description 1 online resource (XV, 287 pages 1 illustrations).
data file rda
Series Graduate Texts in Operations Research, 2662-6020
Graduate texts in operations research, 2662-6020
Summary This open access book demonstrates all the steps required to design heuristic algorithms for difficult optimization. The classic problem of the travelling salesman is used as a common thread to illustrate all the techniques discussed. This problem is ideal for introducing readers to the subject because it is very intuitive and its solutions can be graphically represented. The book features a wealth of illustrations that allow the concepts to be understood at a glance. The book approaches the main metaheuristics from a new angle, deconstructing them into a few key concepts presented in separate chapters: construction, improvement, decomposition, randomization and learning methods. Each metaheuristic can then be presented in simplified form as a combination of these concepts. This approach avoids giving the impression that metaheuristics is a non-formal discipline, a kind of cloud sculpture. Moreover, it provides concrete applications of the travelling salesman problem, which illustrate in just a few lines of code how to design a new heuristic and remove all ambiguities left by a general framework. Two chapters reviewing the basics of combinatorial optimization and complexity theory make the book self-contained. As such, even readers with a very limited background in the field will be able to follow all the content.
Contents Part I: Combinatorial Optimization, Complexity Theory and Problem Modelling -- 1. Elements of Graphs and Complexity Theory -- 2. A Short List of Combinatorial Optimization Problems -- 3. Problem Modelling -- Part II: Basic Heuristic Techniques -- 4. Constructive Methods -- 5. Local Search -- 6. Decomposition Methods -- Part III: Popular Metaheuristics -- 7. Randomized Methods -- 8. Construction Learning -- 9. Local Search Learning -- 10. Population Management -- 11. Heuristics Design -- 12. Codes.
Language English.
Funding Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Bibliography Includes bibliographical references and index.
Access Open access. GW5XE
Local Note Springer Nature Springer Nature - SpringerLink eBooks - Fully Open Access
Subject Operations research.
Mathematical optimization.
Mathematics--Data processing.
Algorithms.
Artificial intelligence.
Operations Research and Decision Theory.
Optimization.
Computational Mathematics and Numerical Analysis.
Algorithms.
Computational Science and Engineering.
Artificial Intelligence.
algorithms.
artificial intelligence.
Metaheuristics
Traveling salesman problem
Indexed Term Algorithms
Heuristics
Travelling Salesman
Local Search
Metaheuristics
Combinatorial Optimization
Artificial Intelligence
In: OAPEN (Open Access Publishing in European Networks) OAPEN
Other Form: 3-031-13713-2
ISBN 3031137140
9783031137143
9783031137136 (print)
Standard No. 10.1007/978-3-031-13714-3 doi
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