Greedy randomized adaptive search procedures

M. G. C. Resende and C. C. Ribeiro

In Handbook of Metaheuristics, F. Glover and G. Kochenberger, eds., Kluwer Academic Publishers, pp. 219-249, 2002


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 minima 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 discuss in detail implementation strategies of memory-based intensification or post-optimization techniques using path-relinking.  Hybridizations with other metaheuristics, parallelization strategies, and applications are also reviewed.

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