Nonlinear Parameter Estimation by Global Optimization - Efficiency and reliability

In this paper we first show that the objective function of a leastsquares type nonlinear parameter estimation problem can be any non-negative real function, and therefore this class of problems corre-sponds to global optimization. Two non-derivative implementationsof a global optimization method are presented; with nine standardtest functions applied to measure their efficiency. A new nonlineartest problem is then presented for testing the reliability of global op-timization algorithms. This test function has a countable infinity oflocal minima and only one global minimizer. The region of attractionof the global minimum is of zero measure. The results of efficiencyand reliability tests are given.