Hybrid Metaheuristics

  • El-Ghazali Talbi

Part of the Studies in Computational Intelligence book series (SCI, volume 434)

Table of contents

  1. Front Matter
    Pages 1-21
  2. Hybrid Metaheuristics for Mono and Multi-objective Optimization, and Optimization under Uncertainty

  3. Combining Metaheuristics with (Complementary)Metaheuristics

    1. Front Matter
      Pages 119-119
    2. Paola Festa, Mauricio G. C. Resende
      Pages 135-155
    3. Una Benlic, Jin-Kao Hao
      Pages 157-185
    4. Sarab Al-Muhaideb, Mohamed El Bachir Menai
      Pages 187-217
    5. Julián Domínguez, Enrique Alba
      Pages 219-235
    6. Christophe Duhamel, Christophe Gouinaud, Philippe Lacomme, Caroline Prodhon
      Pages 237-269
  4. Combining Metaheuristics with Exact Methods from Mathematical Programming Approaches

    1. Front Matter
      Pages 271-271
    2. Andrea Lodi
      Pages 273-284
    3. Filipe Alvelos, Amaro de Sousa, Dorabella Santos
      Pages 285-334
    4. Pedro J. Copado-Méndez, Christian Blum, Gonzalo Guillén-Gosálbez, Laureano Jiménez
      Pages 335-352
    5. A. Mucherino, L. Liberti
      Pages 353-368
    6. Graham Kendall, Barry McCollum, Frederico R. B. Cruz, Paul McMullan, Lyndon While
      Pages 369-385
  5. Combining Metaheuristics with Constraint Programming Approaches

    1. Front Matter
      Pages 387-387
    2. Raffaele Cipriano, Luca Di Gaspero, Agostino Dovier
      Pages 389-414
  6. Combining Metaheuristics with Machine Learning and Data Mining Techniques

    1. Front Matter
      Pages 415-415
    2. Kate Smith-Miles, Brendan Wreford, Leo Lopes, Nur Insani
      Pages 417-432
    3. Tony Wauters, Katja Verbeeck, Patrick De Causmaecker, Greet Vanden Berghe
      Pages 433-452
  7. Back Matter
    Pages 0--1

About this book


The main goal of this book is to provide a state of the art of hybrid metaheuristics. The book provides a complete background that enables readers to design and implement hybrid metaheuristics to solve complex optimization problems (continuous/discrete, mono-objective/multi-objective, optimization under uncertainty) in a diverse range of application domains. Readers learn to solve large scale problems quickly and efficiently combining metaheuristics with complementary metaheuristics, mathematical programming, constraint programming and machine learning. Numerous real-world examples of problems and solutions demonstrate how hybrid metaheuristics are applied in such fields as networks, logistics and transportation, bio-medical, engineering design, scheduling.


Computational Intelligence Hybrid Metaheuristics

Editors and affiliations

  • El-Ghazali Talbi
    • 1
  1. 1., Cite ScientifiqueUniversity of Lille 1Villeneuve d'AscqFrance

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-30670-9
  • Online ISBN 978-3-642-30671-6
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
Industry Sectors
Materials & Steel
Chemical Manufacturing
Finance, Business & Banking
IT & Software
Consumer Packaged Goods
Energy, Utilities & Environment
Oil, Gas & Geosciences