Local Optimization Method with Global Multidimensional

This paper presents a new method for solving global optimization problems. We use a local technique based on the notion of discrete gradients for finding a cone of descent directions and then we use a global cutting angle algorithm for finding global minimum within the intersection of the cone and the feasible region. We present results of numerical experiments with well-known test problems and with the so-called cluster function. These results confirm that the proposed algorithm allows one to find a global minimizer or at least a deep local minimizer of a function with a huge amount of shallow local minima.


Centre for Informatics and Applied Optimization, School of Information Technology and Mathematical Sciences, University of Ballarat, Victoria 3353, Australia, December 2003



View Local Optimization Method with Global Multidimensional