Michel Gendreau
is Department Chair and Professor of Operations Research in the Department of Mathematics and Industrial Engineering of Polytechnique Montréal (Canada). He received his Ph.D. from University of Montreal in 1984. His main research area is the application of operations research methods to a wide range of problem areas: transportation and logistics systems planning and operation, energy production and storage, healthcare, and telecommunications. Dr. Gendreau has published more than 300 papers in peer-reviewed journals and conference proceedings. He was the Editor in chief of Transportation Science from 2009 to 2014 and he is a member of several other editorial boards. Dr. Gendreau has received several research grants and awards, including the Robert Herman Lifetime Achievement Award of the Transportation Science & Logistics Society of INFORMS and the Merit Award of the Canadian Operational Research Society. He was elected Fellow of INFORMS in 2010.
Jean-Yves Potvin
is Professor at Université de Montréal in the Department of Computer Science and Operations Research. He is also Assistant Director of the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). His research integrates operations research and artificial intelligence techniques. More precisely, he is interested in the development of adaptive algorithms based on local search-based metaheuristics, genetic algorithms and neural networks to address different types of vehicle routing problems. He also works on parallel implementations of these algorithms for real-time applications, like dynamic vehicle dispatching.

Provides a complete update to the handbook for the latest developments in metaheuristics methodologies

Offers a broad coverage of the concepts, implementations and applications in this important field of optimization

Includes contributions by renowned experts in metaheuristics

Chapter 1. Simulated Annealing: From Basics to Applications.- Chapter 2. Tabu Search.- Chapter 3. Variable Neighborhood Search.- Chapter 4. Large Neighborhood Search.- Chapter 5. Iterated Local Search: Framework and Applications.- Chapter 6. Greedy Randomized Adaptive Search Procedures: Advances and Extensions.- Chapter 7. Intelligent Multi-Start Methods.- Chapter 8. Next Generation Genetic Algorithms: A User's Guide and Tutorial.- Chapter 9. An Accelerated Introduction to Memetic Algorithms.- Chapter 10. Ant Colony Optimization: Overview and Recent Advances.- Chapter 11. Swarm Intelligence.- Chapter 12. Metaheuristic Hybrids.- Chapter 13. Parallel Metaheuristics and Cooperative Search.- Chapter 14. A Classification of Hyper-heuristic Approaches - Revisited.- Chapter 15. Reactive Search Optimization: Learning while Optimizing.- Chapter 16. Stochastic Search in Metaheuristics.- Chapter 17. Automated Design of Metaheuristic Algorithms.- Chapter 18. Computational Comparison of Metaheuristics.