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.