BDD minimization by scatter search

Reduced-ordered binary decision diagrams (BDDs) are a data structure for representation and manipulation of Boolean functions. The variable ordering largely influences the size of the BDD, varying from linear to exponential. In this paper, the authors study the BDD minimization problem based on scatter search optimization. Scatter search offers a reasonable compromise between quality (BDD reduction) and time. On smaller benchmarks it delivers almost optimal BDD size with less time than the exact algorithm. For larger benchmarks it delivers smaller BDD sizes than genetic algorithm or simulated annealing at the expense of longer runtime.

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