Evolutionary-Reduced Ordered Binary Decision Diagram

Abstract

Reduced ordered binary decision diagram (ROBDD) is a memory-efficient data structure which is used in many applications such as synthesis, digital system, verification, testing and VLSI-CAD. The size of an ROBDD for a function can be increased exponentially by the number of independent variables of the function that is called “memory explosion problem”. The choice of the variable ordering largely influences the size of the OBDD especially for large input variables. Finding the optimal variable ordering is an NP-complete problem, hence, in this paper, two evolutionary methods (GA and PSO) are used to find optimal order of input variable in binary decision diagram. Some benchmarks form LGSynth91 are used to evaluate our suggestion methods. Obtained results show that evolutionary methods have the ability to find optimal order of input variable and reduce the size of ROBDD considerably.