Interactive Dynamic Neighborhood Differential Evolutionary Group Search Optimizer
Group Search Optimizer(GSO) has the advantage of the design from a biological view,while animal scanning mechanisms are employed metaphorically to design optimum searching strategies for solving continuous optimization problems.Compared with some existing group intelligence algorithms,it has a better effect in the high dimensional problems.But from the individual foraging strategies it choose and the entire animal groups information sharing network topology,global optimal possibly missed and information exchange model is simple.Inspiration from the Newman and Watts model,Interactive Dynamic Neighborhood GSO(IDGSO) is proposed based on dynamic sampling.Adopting uniform designand the linear regression method on the parameter selection,4 benchmark functions demonstrate the effectiveness of the algorithm.