Greedy Randomized Adaptive Search Procedures View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1995-03

AUTHORS

Thomas A. Feo, Mauricio G. C. Resende

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. More... »

PAGES

109-133

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf01096763

DOI

http://dx.doi.org/10.1007/bf01096763

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041344187


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