Metaheuristic hybridization with GRASP

Mauricio G. C. Resende(mgcrresearch.att.com)

Abstract: GRASP or greedy randomized adaptive search procedure is a multi-start metaheuristic that repeatedly applies local search starting from solutions constructed by a randomized greedy algorithm. In this paper we consider ways to hybridize GRASP to create new and more effective metaheuristics. We consider several types of hybridizations: constructive procedures, enhanced local search, memory structures, and cost reformulations.

Keywords: GRASP or greedy randomized adaptive search procedure is a multi-start metaheuristic that repeatedly applies local search starting from solutions constructed by a randomized greedy algorithm. In this paper we consider ways to hybridize GRASP to create new and more effective metaheuristics. GRASP, hybrid heuristics, metaheuristics, path-relinking, Lagrangian relaxation, variable neighborhood descent, tabu search, simulated annealing, iterated local search. enhanced local search, memory structures, and cost reformulations.

Category 1: Combinatorial Optimization (Meta Heuristics )

Citation: AT&T Labs Research Technical Report, AT&T Labs Research, Florham Park, NJ, April 14, 2008.

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Entry Submitted: 04/14/2008
Entry Accepted: 04/17/2008
Entry Last Modified: 04/14/2008

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