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Handbook of Metaheuristics

  • Book
  • Dec 2013

Overview

Editors:
  1. Michel Gendreau

    1. , Département de mathématiques et génie in, École Polytechnique Montréal, Montreal, Canada

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  2. Jean-Yves Potvin

    1. Dépt. Informatique et Recherche, Opérationnelle, Université de Montreal, Montreal, Canada

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    You can also search for this editor in PubMed   Google Scholar

  • Covers the important new developments in a fast-moving field
  • New chapters on large neighborhood search, artificial immune systems and hybrid metaheuristics
  • New editors are extremely prominent names in the field
  • Includes supplementary material: sn.pub/extras

Part of the book series: International Series in Operations Research & Management Science (ISOR, volume 146)

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Table of contents (21 chapters)

  1. Front Matter

    Pages i-xix
    Download chapter PDF

  2. Simulated Annealing

    • Alexander G. Nikolaev, Sheldon H. Jacobson
    Pages 1-39

  3. Tabu Search

    • Michel Gendreau, Jean-Yves Potvin
    Pages 41-59

  4. Variable Neighborhood Search

    • Pierre Hansen, Nenad Mladenović, Jack Brimberg, José A. Moreno Pérez
    Pages 61-86

  5. Scatter Search and Path-Relinking: Fundamentals, Advances, and Applications

    • Mauricio G.C. Resende, Celso C. Ribeiro, Fred Glover, Rafael Martí
    Pages 87-107

  6. Genetic Algorithms

    • Colin R. Reeves
    Pages 109-139

  7. A Modern Introduction to Memetic Algorithms

    • Pablo Moscato, Carlos Cotta
    Pages 141-183

  8. Genetic Programming

    • William B. Langdon, Robert I. McKay, Lee Spector
    Pages 185-225

  9. Ant Colony Optimization: Overview and Recent Advances

    • Marco Dorigo, Thomas Stützle
    Pages 227-263

  10. Advanced Multi-start Methods

    • R. Martí, J. Marcos Moreno-Vega, A. Duarte
    Pages 265-281

  11. Greedy Randomized Adaptive Search Procedures: Advances, Hybridizations, and Applications

    • Mauricio G.C. Resende, Celso C. Ribeiro
    Pages 283-319

  12. Guided Local Search

    • Christos Voudouris, Edward P.K. Tsang, Abdullah Alsheddy
    Pages 321-361

  13. Iterated Local Search: Framework and Applications

    • Helena R. Lourenço, Olivier C. Martin, Thomas Stützle
    Pages 363-397

  14. Large Neighborhood Search

    • David Pisinger, Stefan Ropke
    Pages 399-419

  15. Artificial Immune Systems

    • Julie Greensmith, Amanda Whitbrook, Uwe Aickelin
    Pages 421-448

  16. A Classification of Hyper-heuristic Approaches

    • Edmund K. Burke, Matthew Hyde, Graham Kendall, Gabriela Ochoa, Ender Özcan, John R. Woodward
    Pages 449-468

  17. Metaheuristic Hybrids

    • Günther R. Raidl, Jakob Puchinger, Christian Blum
    Pages 469-496

  18. Parallel Meta-heuristics

    • Teodor Gabriel Crainic, Michel Toulouse
    Pages 497-541

  19. Reactive Search Optimization: Learning While Optimizing

    • Roberto Battiti, Mauro Brunato
    Pages 543-571

  20. Stochastic Search in Metaheuristics

    • Walter J. Gutjahr
    Pages 573-597
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Keywords

About this book

The rst edition of the Handbook of Metaheuristics was published in 2003 under the editorship of Fred Glover and Gary A. Kochenberger. Given the numerous - velopments observed in the eld of metaheuristics in recent years, it appeared that the time was ripe for a second edition of the Handbook. For different reasons, Fred and Gary were unable to accept Springer’s invitation to prepare this second e- tion and they suggested that we should take over the editorship responsibility of the Handbook. We are deeply honored and grateful for their trust. As stated in the rst edition, metaheuristics are “solution methods that orch- trate an interaction between local improvement procedures and higher level stra- gies to create a process capable of escaping from local optima and performing a robust search of a solution space. ” Although this broad characterization still holds today, many new and exciting developments and extensions have been observed in the last few years. We think in particular to hybrids, which take advantage of the strengths of each of their individual metaheuristic components to better explore the solution space. Hybrids of metaheuristics with other optimization techniques, like branch-and-bound, mathematical programming or constraint programming are also increasingly popular. On the front of applications, metaheuristics are now used to nd high-quality solutions to an ever-growing number of complex, ill-de ned re- world problems, in particular combinatorial ones.

Editors and Affiliations

  • , Département de mathématiques et génie in, École Polytechnique Montréal, Montreal, Canada

    Michel Gendreau

  • Dépt. Informatique et Recherche, Opérationnelle, Université de Montreal, Montreal, Canada

    Jean-Yves Potvin

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