Metaheuristics

Progress in Complex Systems Optimization

  • Karl F. Doerner
  • Michel Gendreau
  • Peter Greistorfer
  • Walter Gutjahr
  • Richard F. Hartl
  • Marc Reimann

Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 39)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Scatter Search

    1. Front Matter
      Pages 1-1
  3. Tabu Search

    1. Front Matter
      Pages 41-41
    2. Manfred Gronalt, Patrick Hirsch
      Pages 65-88
  4. Nature-inspired methods

    1. Front Matter
      Pages 89-89
    2. Alexander Schirrer, Karl F. Doerner, Richard F. Hartl
      Pages 113-134
  5. GRASP and Iterative Methods

    1. Front Matter
      Pages 135-135
    2. Elizabeth F. Gouvêa Goldbarg, Marco C. Goldbarg, Joã P.F. Farias
      Pages 137-152
  6. Dynamic and Stochastic Problems

    1. Front Matter
      Pages 171-171
    2. Dejan Jovanović, Nenad Mladenović, Zoran Ognjanović
      Pages 173-188
    3. Mauro Birattari, Prasanna Balaprakash, Marco Dorigo
      Pages 189-203
    4. Abdunnaser Younes, Otman Basir, Paul Calamai
      Pages 205-223
    5. Joana Dias, M. Eugénia Captivo, João Clímaco
      Pages 225-244
    6. Enrique Alba, Juan F. Saucedo Badia, Gabriel Luque
      Pages 245-260
    7. Thomas Bartz-Beielstein, Daniel Blum, Jürgen Branke
      Pages 261-273

About this book

Introduction

The aim of METAHEURISTICS: Progress in Complex Systems Optimization is to provide several different kinds of information: a delineation of general metaheuristics methods, a number of state-of-the-art articles from a variety of well-known classical application areas as well as an outlook to modern computational methods in promising new areas. Therefore, this book may equally serve as a textbook in graduate courses for students, as a reference book for people interested in engineering or social sciences, and as a collection of new and promising avenues for researchers working in this field.

Highlighted are recent developments in the areas of Simulated Annealing, Path Relinking, Scatter Search, Tabu Search, Variable Neighborhood Search, Hyper-heuristics, Constraint Programming, Iterated Local Search, GRASP, bio-inspired algorithms like Genetic Algorithms, Memetic Algorithms, Ant Colony Optimization or Swarm Intelligence, and several other paradigms.

Keywords

Analysis Variable algorithm algorithms combinatorial optimization complex systems data mining genetic algorithms metaheuristic optimization programming scheduling tools

Editors and affiliations

  • Karl F. Doerner
    • 1
  • Michel Gendreau
    • 2
  • Peter Greistorfer
    • 3
  • Walter Gutjahr
    • 4
  • Richard F. Hartl
    • 5
  • Marc Reimann
    • 6
  1. 1.University of ViennaAustria
  2. 2.CIRRELTMontréal
  3. 3.Karl-Franzens-Universität GrazAustria
  4. 4.University of ViennaAustria
  5. 5.University of ViennaAustria
  6. 6.ETH ZurichSwitzerland

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-71921-4
  • Copyright Information Springer Science+Business Media, LLC 2007
  • Publisher Name Springer, Boston, MA
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-71919-1
  • Online ISBN 978-0-387-71921-4
  • Series Print ISSN 1387-666X
  • About this book