Greedy Randomized Adaptive Search Procedures

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Greedy Randomized Adaptive Search Procedures (2002)

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by Mauricio G. C. Resende , Celso C. Ribeiro

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644 - 82 self

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@MISC{Resende02greedyrandomized,
    author = {Mauricio G. C. Resende and Celso C. Ribeiro},
    title = {Greedy Randomized Adaptive Search Procedures },
    year = {2002}
}

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Abstract

GRASP is a multi-start metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phase. The best overall solution is kept as the result. In this chapter, we first describe the basic components of GRASP. Successful implementation techniques and parameter tuning strategies are discussed and illustrated by numerical results obtained for different applications. Enhanced or alternative solution construction mechanisms and techniques to speed up the search are also described: Reactive GRASP, cost perturbations, bias functions, memory and learning, local search on partially constructed solutions, hashing, and filtering. We also discuss in detail implementation strategies of memory-based intensification and post-optimization techniques using path-relinking. Hybridizations with other metaheuristics, parallelization strategies, and applications are also reviewed.

Keyphrases

greedy randomized adaptive search procedure local search basic component numerical result feasible solution different application post-optimization technique parallelization strategy successful implementation technique local minimum alternative solution construction mechanism combinatorial problem multi-start metaheuristic detail implementation strategy construction phase reactive grasp cost perturbation memory-based intensification local search phase overall solution bias function parameter tuning strategy

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