A hybrid heuristic method for global optimization

In this paper a new stochastic hybrid technique for unconstrained global optimization (GO) is proposed. It is a combination of an iterative algorithm developed by us (called LP/sub /spl tau//O) that uses low-discrepancy sequences of points and heuristic knowledge to find regions of attraction when searching for a global minimum (GM) and the well-known Nelder-Mead simplex local search. The combination of the two techniques provides a powerful hybrid optimization tool that we call LP/sub /spl tau//SS. The proposed LP/sub /spl tau//SS method is tested on a number of multimodal mathematical functions and results are discussed and compared with such from other stochastic methods.