BDD minimization based on genetic tabu hybrid strategy

We present a new BDD minimization algorithm based on a genetic tabu hybrid strategy which combines the global space search of genetic algorithm with the local neighborhood search and the gradual global optimizing of tabu search. The variables swap and the uniform distribution are utilized to generate neighborhood solutions in the search space. Experiment results are given to demonstrate the efficiency of the presented approach. A comparison to other minimization algorithms is reported. The future work will be focused on how to improve the convergence speed of the new algorithm