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Journal of Global Optimization

, Volume 6, Issue 2, pp 109–133 | Cite as

Greedy Randomized Adaptive Search Procedures

  • Thomas A. Feo
  • Mauricio G. C. Resende
Article

Abstract

Today, a variety of heuristic approaches are available to the operations research practitioner. One methodology that has a strong intuitive appeal, a prominent empirical track record, and is trivial to efficiently implement on parallel processors is GRASP (Greedy Randomized Adaptive Search Procedures). GRASP is an iterative randomized sampling technique in which each iteration provides a solution to the problem at hand. The incumbent solution over all GRASP iterations is kept as the final result. There are two phases within each GRASP iteration: the first intelligently constructs an initial solution via an adaptive randomized greedy function; the second applies a local search procedure to the constructed solution in hope of finding an improvement. In this paper, we define the various components comprising a GRASP and demonstrate, step by step, how to develop such heuristics for combinatorial optimization problems. Intuitive justifications for the observed empirical behavior of the methodology are discussed. The paper concludes with a brief literature review of GRASP implementations and mentions two industrial applications.

Key words

Combinatorial optimization search heuristic GRASP computer implementation 

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Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Thomas A. Feo
    • 1
  • Mauricio G. C. Resende
    • 2
  1. 1.Operations Research Group, Department of Mechanical EngineeringThe University of TexasAustinUSA
  2. 2.Mathematical Sciences Research CenterAT&T Bell LaboratoriesMurray HillUSA

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