© 2004

Metaheuristics: Computer Decision-Making


Part of the Applied Optimization book series (APOP, volume 86)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Laurent Alfandari, Agnès Plateau, Pierre Tolla
    Pages 1-17
  3. Mousbah Barake, Pierre Chardaire, Geoff P. McKeown
    Pages 19-36
  4. Alexandre Belloni, Abilio Lucena
    Pages 37-63
  5. Urszula Boryczka, Mariusz Boryczka
    Pages 91-125
  6. Edmund Burke, Patrick De Causmaecker, Sanja Petrovic, Greet Vanden Berghe
    Pages 153-172
  7. Marco P. Carrasco, Margarida V. Pato
    Pages 173-186
  8. Maria João Cortinhal, Maria Eugénia Captivo
    Pages 187-216
  9. Lino Costa, Pedro Oliveira
    Pages 217-236
  10. Raphaël Dorne, Christos Voudouris
    Pages 237-256
  11. Zvi Drezner, George A. Marcoulides
    Pages 257-278
  12. Jean-Philippe Hamiez, Jin-Kao Hao
    Pages 325-345
  13. B. de la Iglesia, J. J. Wesselink, V. J. Rayward-Smith, J. Dicks, I. N. Roberts, V. Robert et al.
    Pages 347-367
  14. Joanna Józefowska, Grzegorz Waligóra, Jan Węglarz
    Pages 385-404

About this book


Combinatorial optimization is the process of finding the best, or optimal, so­ lution for problems with a discrete set of feasible solutions. Applications arise in numerous settings involving operations management and logistics, such as routing, scheduling, packing, inventory and production management, lo­ cation, logic, and assignment of resources. The economic impact of combi­ natorial optimization is profound, affecting sectors as diverse as transporta­ tion (airlines, trucking, rail, and shipping), forestry, manufacturing, logistics, aerospace, energy (electrical power, petroleum, and natural gas), telecommu­ nications, biotechnology, financial services, and agriculture. While much progress has been made in finding exact (provably optimal) so­ lutions to some combinatorial optimization problems, using techniques such as dynamic programming, cutting planes, and branch and cut methods, many hard combinatorial problems are still not solved exactly and require good heuristic methods. Moreover, reaching "optimal solutions" is in many cases meaningless, as in practice we are often dealing with models that are rough simplifications of reality. The aim of heuristic methods for combinatorial op­ timization is to quickly produce good-quality solutions, without necessarily providing any guarantee of solution quality. Metaheuristics are high level procedures that coordinate simple heuristics, such as local search, to find solu­ tions that are of better quality than those found by the simple heuristics alone: Modem metaheuristics include simulated annealing, genetic algorithms, tabu search, GRASP, scatter search, ant colony optimization, variable neighborhood search, and their hybrids.


Computer Operations Research Operator algorithm algorithms classification communication computer science database genetic algorithms heuristics learning optimization programming

Authors and affiliations

  1. 1.AT&T Labs — ResearchUSA
  2. 2.INESCPortoPortugal

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