An Ant-Based Framework for Very Strongly Constrained Problems

Authors:
Vittorio Maniezzo;Matteo Milandri
Affiliations:
-;-
Venue:
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Year:
2002

Citing 3
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Abstract

Metaheuristics in general and ant-based systems in particular have shown remarkable success in solving combinatorial optimization problems. However, a few problems exist for which the best performing heuristic algorithm is not a metaheuristic. These few are often characterized by a very highly constrained search space. This is a situation in which it is not possible to define any efficient neighborhood, thus no local search is available. The paradigmatic case is the set partitioning problem, a problem for which standard Integer Programming solvers outperform metaheuristics. This paper presents an extended ant framework improving the effectiveness of ant-based systems to such problems. Computational results are presented both on standard set partitioning problem instances and on vertical fragmentation problem instances. This last is a real world problem arising in data warehouse logical design.