An Adaptive Symbiosis based Metaheuristics for Combinatorial Optimization in VLSI

Abstract

The elevated perplexity in solving non deterministic problems (NP-Hard) has opened the way for the emergence of several metaheuristics. Most of the real time problems pertaining to electronics are Multi-Objective Optimization Problem (MOOP). Hence, there is always a conflict between several tradeoffs. While delving in to past anthologies, it is evident that biologically inspired algorithm tend to solve NP-Hard problems in a more appreciable manner since nature optimizes at the best. This research work contemplates the problem of VLSI floorplanning and solves the mentioned problem with a symbiosis based metaheuristics. Symbiosis is a natural phenomenon wherein organisms interact amongst themselves for survival. A usual symbiosis has three phenomenon packed: Mutualism phase, Commensalism phase and parasitism phase. However, in the adaptive mechanism there is a deciding factor which decides which phase has to commence. The problem was tested with MCNC benchmarks and has yielded palpable results over few other algorithms under test. With the proposed algorithm, in the problem of VLSI floorplanning, wirelength and area were considered as the objectives that were optimized which eventually resulted in the minimization of dead space (unused space) in the circuit