An Evolutionary Algorithm with Stochastic Hill-Climbing for the Edge-Biconnectivity Augmentation Problem
Augmenting an existing network with additional links to achieve higher robustness and survivability plays an important role in network design. We consider the problem of augmenting a network with links of minimum total cost in order to make it edge-biconnected, i.e. the failure of a single link will never disconnect any two nodes. A new evolutionary algorithm is proposed that works directly on the set of additional links of a candidate solution. Problem-specific initialization, recombination, and mutation operators use a stochastic hill-climbing procedure. With low computational effort, only locally optimal, feasible candidate solutions are produced. Experimental results are significantly better than those of a previous genetic algorithm concerning final solutions’ qualities and especially execution times.
KeywordsEvolutionary Algorithm Mutation Operator Minimum Span Tree Memetic Algorithm Hybrid Genetic Algorithm
Unable to display preview. Download preview PDF.
- 2.Ivana LjubiĆ, Günther R. Raidl, and Jozef Kratica. A hybrid GA for the edge-biconnectivity augmentation problem. In Kalyanmoy Deb, Günther Rudolph, Xin Yao, and Hans-Paul Schwefel, editors, Proceedings of the 2000 Parallel Problem Solving from Nature VI Conference, volume 1917 of LNCS, pages 641–650. Springer, 2000.Google Scholar
- 7.A. Zhu. A uniform framework for approximating weighted connectivity problems. B.Sc. thesis, University of Maryland, MD, May 1999.Google Scholar
- 8.A. Zhu, S. Khuller, and B. Raghavachari. A uniform framework for approximating weighted connectivity problems. In Proceedings of the 10th ACM-SIAM Symposium on Discrete Algorithms, pages 937–938, 1999.Google Scholar
- 11.P. Moscato. Memetic algorithms: A short introduction. In D. Corne et al., editor, New Ideas in Optimization, pages 219–234. McGraw Hill, Berkshire, England, 1999.Google Scholar