Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
BRITE: An Approach to Universal Topology Generation
MASCOTS '01 Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems
A study of ACO capabilities for solving the maximum clique problem
Journal of Heuristics
A PTAS for the minimum dominating set problem in unit disk graphs
WAOA'05 Proceedings of the Third international conference on Approximation and Online Algorithms
Hybrid genetic algorithm for minimum dominating set problem
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part IV
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Minimum dominating set, which is an NP-hard problem, finds many practical uses in diverse domains. A greedy algorithm to compute the minimum dominating set is proven to be the optimal approximate algorithm unless P=NP. Meta-heuristics, generally, find solutions better than simple greedy approximate algorithms as they explore the search space better without incurring the cost of an exponential algorithm. However, there are hardly any studies of application of meta-heuristic techniques for this problem. In some applications it is important to minimize the dominating set as much as possible to reduce cost and/or time to perform other operations based on the dominating set. In this paper, we propose a hybrid genetic algorithm and an ant-colony optimization (ACO) algorithm enhanced with local search. We compare the performance of these two hybrid algorithms against the solutions obtained using the greedy heuristic and another hybrid genetic algorithm proposed in literature. We find that the ACO algorithm enhanced with a minimization heuristic performs better than all other algorithms in almost all instances.