Group search optimizer with interactive dynamic neighborhood

Abstract

Group search optimizer(GSO) is a new novel optimization algorithm by simulating animal behaviour. It uses the Gbest topology structure, which leads to rapid exchange of information among particles. So,it is easily trapped into a local optima when dealing with multi-modal optimization problems. In this paper,inspiration from the Newman and Watts model,a improved group search optimizer with interactive dynamic neighborhood (IGSO) is proposed. Adopting uniform design and the linear regression method on the parameter selection, four benchmark functions demonstrate the effectiveness of the algorithm. © 2011 Springer-Verlag.