A Hybrid Estimation of Distribution Algorithm for the Minimal Switching Graph Problem
Tang, Maolin & Lau, Raymond Y. K. (2006) A Hybrid Estimation of Distribution Algorithm for the Minimal Switching Graph Problem. In International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06), 28-30 November 2006, Vienna, Austria.
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.
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|Item Type:||Conference Paper|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2006 IEEE|
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|Deposited On:||11 Sep 2007 00:00|
|Last Modified:||29 Feb 2012 13:21|
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