Variable Ordering in Binary Decision Diagram Using Spider Monkey Optimization for Node and Path Length Optimization

Binary Decision Diagrams (BDDs) are an important data structure for the design of digital circuits using VLSI CAD tools. The ordering variables affects total number nodes and path length in BDDs. Finding a good variable is optimization problem previously many approaches have been implemented BDDs research works. In this paper, approach based on Spider Monkey Optimization (SMO) algorithm proposed BDD targeting longest length. SMO well-known swarm intelligence-based spider monkeys foraging behavior. work has compared with other latest reordering Particle Swarm (PSO) algorithm. results obtained show significant improvement over method. SMO-based method applied to different benchmark having levels complexities. node count maximum tested found be better than PSO.