Simulated Evolution Algorithm For Multiobjective VLSI Netlist Bi-Partitioning

Abstract

In this paper, the Simulated Evolution algorithm (SimE) is engineered to solve the optimization problem of multi-objective VLSI netlist bi-partitioning. The multi-objecttive version of the problem is addressed in which power disssipation, timing performance, as well as cut set are optimized while balance is taken as a constraint. Fuzzy rules are used in order to design the overalll multi-objective cost function that integrates the costs of three objectives in a single overall cost value. Fuzzy goodnesss functions are designed for delay and power, and proved efficient. A series of experiments are performed to evaluate the efficiency of the algorithm. ISCAS-85/89 benchmark circuits are used and experimental results are reported and compared to earliar algorithms like GA and TS.