The problem of partitioning appears in several areas ranging 
from VLSI, parallel programming, to molecular biology. The 
interest in achieving better partitioning of any system is 
growing rapidly especially in VLSI, and has been a hot issue 
in recent years. In VLSI circuit partitioning, the problem of 
obtaining a minimum cut has been of prime importance and most 
literature available is for this single objective optimization. 
However, with current technology trends partitioning has become 
a multi-objective problem (MOP), where power and delay, in 
addition to minimum cut, need to be optimized. In this thesis, 
the multi-objective optimization problem at the partitioning 
phase in VLSI physical design step is addressed. This problem 
involves multiple, possibly conflicting objectives; hence fuzzy 
rules have been incorporated in designing the overall cost 
function. Iterative algorithms, namely Genetic Algorithm (GA), 
Tabu Search (TS), and Simulated Evolution (SimE) are tailored 
for finding good quality solutions for the above mentioned MOP 
problem. Another deterministic algorithm PowerFm which is an 
extension to Fidducia Mattheyeses (FM) heuristic is proposed 
and results compared and analyzed.