A very large-scale integration global routing optimization model for hybrid physarum bionetworks

Interconnection of billion transistors in a single layer of a die with the advent of the nanometer regime imposes a great challenge to handle the increased complexity, particularly in the global routing of the Very Large-Scale Integration (VLSI) physical design phase which involves distinct optimization in the computation of overall interconnect wire-length. In classical graph theory, the VLSI global routing problem can be mapped as a Rectilinear Steiner Minimal Tree (RSMT) Problem, which in itself is an NP-complete problem. The use of metaheuristics in solving this problem plays a major role where Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) proved to be efficient algorithms. Both algorithms face certain limitations and inconsistencies in determining maximum optimization. A new optimization algorithm with insight from the biological activity of microorganisms has been proposed in this paper which is based on the behavior of the unicellular organism Physarum Polycephalum aiming at minimizing the wire length of VLSI interconnects. The paper further explores a new hybridization technique employing the use of Physarum BioNetwork and Particle Swarm Optimization together where PSO generate better possible Steiner’s in the initial stage for the final process using Physarum BioNetwork to ensure better convergence. Complexity analysis of the proposed algorithm has been performed and the simulation results achieved greater efficiency when compared with the conventional PSO algorithm and available industry benchmark over-optimizing Global Routing problem in VLSI design.