  A novel stochastic optimization method called MAC was suggested. The method
is based on the calculation of the objective function at several random points
and then an empirical expected value and an empirical covariance matrix are
calculated. The empirical expected value is proven to converge to the optimum
value of the problem. The MAC algorithm was encoded in Matlab and the code was
tested on 20 test problems. Its performance was compared with those of the
interior point method (Matlab name: fmincon), simplex, pattern search (PS),
simulated annealing (SA), particle swarm optimization (PSO), and genetic
algorithm (GA) methods. The MAC method failed two test functions and provided
inaccurate results on four other test functions. However, it provided accurate
results and required much less CPU time than the widely used optimization
methods on the other 14 test functions.
