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Hybrid GRASP Heuristics

  • Paola Festa
  • Mauricio G. C. Resende
Part of the Studies in Computational Intelligence book series (SCI, volume 203)

Abstract

Experience has shown that a crafted combination of concepts of different metaheuristics can result in robust combinatorial optimization schemes and produce higher solution quality than the individual metaheuristics themselves, especially when solving difficult real-world combinatorial optimization problems. This chapter gives an overview of different ways to hybridize GRASP (Greedy Randomized Adaptive Search Procedures) to create new and more effective metaheuristics. Several types of hybridizations are considered, involving different constructive procedures, enhanced local search algorithms, and memory structures.

Keywords

Local Search Tabu Search Variable Neighborhood Search Scatter Search Iterate Local Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Paola Festa
    • 1
  • Mauricio G. C. Resende
    • 2
  1. 1.Dept. of Mathematics and ApplicationsUniversity of Napoli FEDERICO IINaplesItaly
  2. 2.Algorithms and Optimization Research Dept.AT&T Labs ResearchFlorham ParlUSA

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