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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 traac assignment in communication satellites. We review basic concepts of GRASP: construction and local search algorithms. The local search phase is based on(More)
Given an undirected graph with prizes associated with its nodes and weights associated with its edges, the prize-collecting Steiner tree problem consists in finding a subtree of this graph which minimizes the sum of the weights of its edges plus the prizes of the nodes not spanned. In this paper, we describe a multi-start local search algorithm for the(More)
GRASP is a multi-start metaheuristic for combinatorial optimization 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(More)
We propose and describe a hybrid GRASP with weight perturbations and adaptive path-relinking heuristic (HGP-PR) for the Steiner problem in graphs. In this multi-start approach, the greedy randomized construction phase of a GRASP is replaced by the combination of several construction heuristics with a weight perturbation strategy that combines(More)
Path-relinking is a major enhancement to the basic greedy randomized adap-tive search procedure (GRASP), leading to significant improvements in solution time and quality. Path-relinking adds a memory mechanism to GRASP by providing an intensifi-cation strategy that explores trajectories connecting GRASP solutions and the best elite solutions previously(More)
This papers describes a perl language program to create time-to-target solution value plots for measured CPU times that are assumed to fit a shifted exponential distribution. This is often the case in local search based heuristics for combinatorial optimization , such as simulated annealing, genetic algorithms, iterated local search, tabu search, WalkSAT,(More)
We propose in this work a hybrid improvement procedure for the bin packing problem. This heuristic has several features: the use of lower bounding strategies; the generation of initial solutions by reference to the dual min-max problem; the use of load redistribution based on dominance, differencing, and unbalancing; and the inclusion of an improvement(More)
A GRASP (greedy randomized adaptive search procedure) is a multi-start metaheuristic for combinatorial optimization. We study the probability distributions of solution time to a sub-optimal target value in five GRASPs that have appeared in the literature and for which source code is available. The distributions are estimated by running 12,000 independent(More)