Computational Intelligence for Modelling, Control and Automation, International Conference on (2005)
Nov. 28, 2005 to Nov. 30, 2005
Maolin Tang , Queensland University of Technology, Australia
Raymond Y. K. Lau , City University of Hong Kong
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.
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.