Future Generation Computer Systems
Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms
Statistics and Computing
Ant Colony Optimization
A complete anytime algorithm for treewidth
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Towards fixed-parameter tractable algorithms for abstract argumentation
Artificial Intelligence
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Instances of constraint satisfaction problems can be solved efficiently if they are representable as a tree decomposition of small width. Unfortunately, the task of finding a decomposition of minimum width is NP-complete itself. Therefore, several heuristic and metaheuristic methods have been developed for this problem. In this paper we investigate the application of different variants of Ant Colony Optimization algorithms for the generation of tree decompositions. Furthermore, we extend these implementations with two local search methods and we compare two heuristics that guide the ACO algorithms. Our computational results for selected instances of the DIMACS graph coloring library show that the ACO metaheuristic gives results comparable to those of other decomposition methods such as branch and bound and tabu search for many problem instances. One of the proposed algorithms was even able to improve the best known upper bound for one problem instance. Nonetheless, for some larger problems the best existing methods outperform our algorithms.