Feedback loop mechanisms based particle swarm optimization with neighborhood topology

Particle swarm optimization (PSO) is an optimization approach and has been widely used for a verity of optimization problem in both research and industrial domains. Due to the potential of PSO, several variants of the original PSO algorithms have been developed to improve PSO´s efficiency and robustness. This paper proposes another variant of particle swarm optimization algorithm, called N-PωSO. This N-PωSO algorithm is based on classical feedback control theory and topological neighborhood, which offers better search efficiency and convergence stability. As a result, our N-PuωSO method features faster searching from the proportional term without steady-state error. And empirical results show that our N-PωSO algorithm is able to achieve high performance for both unimodal and multimodal optimization problems.