An Elitist GRASP Metaheuristic for the Multi-objective Quadratic Assignment Problem
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
A multiobjective GRASP for rule selection
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Very large-scale neighborhood search for solving multiobjective combinatorial optimization problems
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
A hybrid evolutionary metaheuristics (HEMH) applied on 0/1 multiobjective knapsack problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
GRASP strategies for a bi-objective commercial territory design problem
Journal of Heuristics
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In this article, we propose a Greedy Randomized Adaptive Search Procedure (GRASP) to generate a good approximation of the efficient or Pareto optimal set of a multi-objective combinatorial optimization problem. The algorithm is based on the optimization of all weighted linear utility functions. In each iteration, a preference vector is defined and a solution is built considering the preferences of each objective. The found solution is submitted to a local search trying to improve the value of the utility function. In order to find a variety of efficient solutions, we use different preference vectors, which are distributed uniformly on the Pareto frontier. The proposed algorithm is applied for the 0/1 knapsack problem with r = 2, 3, 4 objectives and n = 250, 500, 750 items. The quality of the approximated solutions is evaluated comparing with the solutions given by three genetic algorithms from the literature.