Stochastic Diffusion Search for Continuous Global Optimization

Stochastic Diffusion Search (SDS) is a multi-agent, naturally inspired search and optimization algorithm that is based on direct (one-to-one) communication between agents. SDS has been successfully applied to a wide range of optimization problems. In this paper, SDS is used to tackle the continuous nonlinear function optimization problem. The proposed method is tested on a minibenchmark of four problems with promising results. The results show that SDS can also be used as an intelligent way to choose a starting point for a local search method.