We present a stochastic global optimization method that employs a clustering technique which is based on a typical distance and a gradient test. The method aims to recover all the local minima inside a rectangular domain. A new stopping rule is used. Comparative results on a set of test functions are reported.
Citation
Preprint, no 4-5/2004 Dept. of Computer Science, University of Ioannina, Greece