Abstract:
A classical problem in VLSI floorplanning is to arrange a set of rectangular modules on a
rectangular chip area ensuring that no two modules overlap with each other which provide
an optimum measure of performance. In our thesis we incorporate different metaheuristic
algorithms to solve the VLSI floorplanning problems which produce different results according
to the applied algorithms. While applying these algorithms we incorporate the use
of an effective method that is Clonal Selection Algorithm (CSA) with mutation operator to
explore the search space. Then these algorithms also use a number of single state methods
separately, to exploit the search space. This incorporation of CSA instead of other methods
like crossover is a novel work in this area. Experimental results show that our metaheuristic
algorithms provide better result than those algorithms which use other metaheuristic algorithms.
In our thesis, we use the existing popular benchmarks with a view to comparing our
results with the previous research works. Hence, our algorithms produce optimal or nearly
optimal solutions for all the tested benchmark problems. Moreover, we develop visualization
software, incorporating all our algorithms, along with graphical output of floorplans,
which can show different comparative results on basis of statistical test and Kruskal-Wallis
test for different applied algorithms.
