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Comparison of BDD Node_Count results in top 16 circuits for four different population sizes ( 50, 100, 150, and 200) for SMO (Average of 30 simulations results for each of the four population sizes on every circuit).

Comparison of BDD Node_Count results in top 16 circuits for four different population sizes ( 50, 100, 150, and 200) for SMO (Average of 30 simulations results for each of the four population sizes on every circuit).

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Fig. 1 Reduction rules for Binary Decision Diagrams.
Fig. 2 Construction of the BDD for the function: z = (¬(a ∧ b ∧ c)) ∨...
Fig. 5 A solution vector represented by an individual spider monkey S M i .
Fig. 7 A valid BDD solution generated using position-based sorting for...
+3 Fig. 8 Comparison of BDD Node_Count results in top 16 circuits for four...
Variable Ordering in Binary Decision Diagram Using Spider Monkey Optimization for Node and Path Length Optimization
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  • Jan 2023
Binary Decision Diagrams (BDDs) are an important data structure for the design of digital circuits using VLSI CAD tools. The ordering of variables affects the total number of nodes and path length in the BDDs. Finding a good variable ordering is an optimization problem and previously many optimization approaches have been implemented for BDDs in a...
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Context 1
... fix swarm size (N), each circuit is run with four different values of (N):50, 100, 150, and 200 to calculate the fitness function fitness_Node. For each value of N, each circuit is simulated 30 times and an average of 30 runs are shown in Fig. 8. From Fig. 8, we have observed that fixing swarm size (N) 100 will be the best choice as it is clear that using a lower swarm size (N), 50, shows a a slight decrement in result quality in many circuits. Also from Fig. 8, fixing the swarm size (N) to 150 and 200 has no significant improvement in result quality in most of the ...
Context 2
... fitness function fitness_Node. For each value of N, each circuit is simulated 30 times and an average of 30 runs are shown in Fig. 8. From Fig. 8, we have observed that fixing swarm size (N) 100 will be the best choice as it is clear that using a lower swarm size (N), 50, shows a a slight decrement in result quality in many circuits. Also from Fig. 8, fixing the swarm size (N) to 150 and 200 has no significant improvement in result quality in most of the ...
Context 3
... fix swarm size (N), each circuit is run with four different values of (N):50, 100, 150, and 200 to calculate the fitness function fitness_Node. For each value of N, each circuit is simulated 30 times and an average of 30 runs are shown in Fig. 8. From Fig. 8, we have observed that fixing swarm size (N) 100 will be the best choice as it is clear that using a lower swarm size (N), 50, shows a a slight decrement in result quality in many circuits. Also from Fig. 8, fixing the swarm size (N) to 150 and 200 has no significant improvement in result quality in most of the circuits. ...
Context 4
... fitness function fitness_Node. For each value of N, each circuit is simulated 30 times and an average of 30 runs are shown in Fig. 8. From Fig. 8, we have observed that fixing swarm size (N) 100 will be the best choice as it is clear that using a lower swarm size (N), 50, shows a a slight decrement in result quality in many circuits. Also from Fig. 8, fixing the swarm size (N) to 150 and 200 has no significant improvement in result quality in most of the circuits. ...