Design of Heuristic Algorithms for Hard Optimization

With Python Codes for the Travelling Salesman Problem
1. Auflage 2023
ISBN: 978-3-031-13716-7
Verlag: Springer International Publishing

Buch, Englisch, 287 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 464 g

Reihe: Graduate Texts in Operations Research

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, whichillustrate 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.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Taillard, Éric D.

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

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.

Über Autor(innen)

Éric D. Taillard is a professor at the University of Applied Sciences and Arts of Western Switzerland, HEIG-VD campus in Yverdon-les-Bains. After completing his studies and obtaining a PhD at the Swiss Federal Institute of Technology in Lausanne, he worked as a researcher at the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation in Montreal, Canada, and then at the Dalle Molle Institute for Artificial Intelligence in Lugano, Switzerland.
He has over 30 years of research experience in the field of metaheuristics. Outside of Switzerland, he has been invited to teach this subject at various universities: Vienna and Graz in Austria, Nantes in France and Hamburg in Germany.