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.

No abstract

A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorial optimization. In this paper, we describe a GRASP for a matrix decomposition problem arising in the context of traffic assignment in communication satellites. We review basic concepts of GRASP: construction and local search algorithms. The local search phase is based on the use of a new type of neighborhood defined by constructive and destructive moves. The implementation of a GRASP for the matrix decomposition problem is described in detail. Extensive computational experiments on literature and randomly generated problems are reported. Moreover, we propose a new procedure Reactive GRASP, in which the basic parameter that defines the restrictiveness of the candidate list during the construction phase is self-adjusted according to the quality of the solutions previously found. The approach is robust and does not require calibration efforts. On most of the literature problems considered, the new Reactive GRASP heuristic matches the optimal solution found by an exact column-generation with branch-and-bound algorithm.

No abstract

No abstract