Mean Field Annealing (MFA) merges collective computation and annealing
properties of Hopfield Neural Networks (HNN) and Stochastic Simulated
Annealing (SSA), respectively, to obtain a general algorithm for solving
combinatorial optimization problems. Mean Field Annealing is a
deterministic approximation, using mean field theory and stochastic
simulated annealing. Since MFA is deterministic in nature, this gives the
advantage of faster convergence to the equilibrium temperature, compared
to stochastic simulated annealing. The mathematics of MFA is shown to
provide a powerful and general tool for deriving optimization algorithms. In
this paper, the MFA concepts are studied, the mathematics of MFA are
derived, and different response functions are used to implement the MFA
algorithm. Experimental results are implemented using different network
topologies on a real classification problem known as Graph bipartitioning
which was applied on Circuit Bi-partitioning. A comparative approach
using the different response functions is applied. Two annealing schedules
namely: the Cauchy annealing schedule and the linear annealing schedule
are used and compared. The study and results are encouraging and
promising.