Two Hybrid Meta-heuristic Approaches for Minimum Dominating Set Problem
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.
KeywordsAnt-Colony Optimization Genetic Algorithm Heuristic Minimum Dominating Set
Unable to display preview. Download preview PDF.
- 3.Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)Google Scholar
- 5.Mastrogiovanni, M.: The clustering simulation framework: A simple manual (2007), http://www.michele-mastrogiovanni.net/software/download/README.pdf
- 6.Medina, A., Lakhina, A., Matta, I., Byers, J.: Brite user manual, http://www.cs.bu.edu/brite/user_manual/node42.html
- 7.Medina, A., Lakhina, A., Matta, I., Byers, J.: Brite: An approach to universal topology generation. In: Proceedings of the International Workshop on Modeling, Analysis and Simulation of Computer and Telecommunications Systems, MASCOTS 2001 (2001)Google Scholar
- 13.Wattenhoffer, R.: Distributed dominating set approximation, http://www.disco.ethz.ch/lectures/ss04/distcomp/lecture/chapter12.pdf