A clustering global optimization method for parameter estimation problems

In this paper it is first shown that the objective function of a least squares type nonlinear parameter estimation problem can be any non-negative real function, and therefore this class of problems corresponds to the global optimization. Two non-derivative implementations of a global optimization method are presented; with nine standard test functions applied to measure their efficiency. A new nonlinear test problem is then presented for testing the reliability of global optimization algorithms. This test function has a countable infinity of local minima and only one global minimizer. The region of attraction of the global minimum is of zero measure. The results of efficiency and reliability tests are given, and three local search procedures are compared by means of real life problem.