Design of Heuristic Algorithms for Hard Optimization

With Python Codes for the Travelling Salesman Problem

AvÉric D. Taillard

Inbunden, Engelska, 2022

Del i serien Graduate Texts in Operations Research

542 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Fler format och utgåvor

Häftad

435 kr

Beskrivning

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.

Produktinformation

Utgivningsdatum:2022-10-30
Mått:155 x 235 x 24 mm
Vikt:598 g
Format:Inbunden
Språk:Engelska
Serie:Graduate Texts in Operations Research
Antal sidor:287
Förlag:Springer International Publishing AG
ISBN:9783031137136

Utforska kategorier

Mer om författaren

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.

Innehållsförteckning

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.