Global optimization by excitable walkers

Abstract

A new global optimization algorithm, based on the concept of excitable walkers, is proposed. The walkers perform parallel Monte Carlo walks on the locally minimized potential energy surface, and effectively repel each other in an appropriate order parameter space. The algorithm is applied to different nanocluster systems (Lennard–Jones and binary metallic clusters) and is proved to be very efficient in locating the global minima of multiple-funnel potential energy surfaces.