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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 the graph planarization problem, extending the heuristic of Goldschmidt and Takvorian [Networks 24 (1994) 69–73]. We review the basic concepts of GRASP: construction and local search algorithms. The implementation of GRASP for graph planarization is described in detail. Computational experience on a large set of standard test problems is presented. On almost all test problems considered, the new heuristic either matches or finds a better solution than previously described graph planarization heuristics. In several cases, previously unknown optima solutions are found.
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Resende et al. (1997) studied this question.
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