The comparison was conducted using test functions collected by Dr. Eric Sandgren in his dissertation (Sandgren, Eric. “The Utility of Nonlinear Programming Algorithms”, a thesis submitted for the Degree of Doctor of Philosophy, Purdue Univercity, 1977). By superimposing the multiplicative numerical noise on the main function we simulated stochastic behavior. The numerical noise was distributed according to normal distribution:

Y_sto=Y_ini*(1+N(0,s)).

We compared four different optimization methods:

IOSO (version IOSO NS 1.0);

MFD - modified method of feasible directions;

SQP - sequental quadratic programming;

SLP - sequental linear programming.