Computational Intelligence for Modelling, Control and Automation, International Conference on (2005)

Vienna, Austria

Nov. 28, 2005 to Nov. 30, 2005

ISBN: 0-7695-2504-0

pp: 708-713

Maolin Tang , Queensland University of Technology, Australia

Raymond Y. K. Lau , City University of Hong Kong

ABSTRACT

Minimal Switching Graph (MSG) is a graphical model for the constrained via minimization problem?a combinatorial optimization problem in integrated circuit design automation. From a computational point of view, the problem is NP-complete. In this paper we present a new approach to the MSG problem using hybrid Estimation of Distribution Algorithms (EDAs). This approach uses a Univariate Marginal Distribution Algorithm (UMDA) to sample start search points and employs a hill-climbing algorithm to find a local optimum in the basins where the start search points are located. By making use of the efficient exploration of the UMDA and the effective exploitation of the hill-climbing algorithm, this hybrid EDA can find an optimal or nearoptimal solution efficiently and effectively. The hybrid EDA has been implemented and compared with the UMDA and the hill-climbing algorithm. Experimental results show that the hybrid EDA significantly outperforms both the UMDA and the hill-climbing algorithm.

INDEX TERMS

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CITATION

R. Y. Lau and M. Tang, "A Hybrid Estimation of Distribution Algorithm for the Minimal Switching Graph Problem,"

*Computational Intelligence for Modelling, Control and Automation, International Conference on(CIMCA)*, Vienna, Austria, 2005, pp. 708-713.

doi:10.1109/CIMCA.2005.1631347

CITATIONS