  By formulating the floorplanning of VLSI as a mixed-variable optimization
problem, this paper proposes to solve it by memetic algorithms, where the
discrete orientation variables are addressed by the distribution evolutionary
algorithm based on a population of probability model (DEA-PPM), and the
continuous coordination variables are optimized by the conjugate sub-gradient
algorithm (CSA). Accordingly, the fixed-outline floorplanning algorithm based
on CSA and DEA-PPM (FFA-CD) and the floorplanning algorithm with golden section
strategy (FA-GSS) are proposed for the floorplanning problems with and without
fixed-outline constraint. %FF-CD is committed to optimizing wirelength targets
within a fixed profile. FA-GSS uses the Golden Section strategy to optimize
both wirelength and area targets. The CSA is used to solve the proposed
non-smooth optimization model, and the DEA-PPM is used to explore the module
rotation scheme to enhance the flexibility of the algorithm. Numerical
experiments on GSRC test circuits show that the proposed algorithms are
superior to some celebrated B*-tree based floorplanning algorithms, and are
expected to be applied to large-scale floorplanning problems due to their low
time complexity.
