  Particle swarm optimization (PSO) is a well-known optimization algorithm that
shows good performance in solving different optimization problems. However, PSO
usually suffers from slow convergence. In this article, a reinforcement
learning-based online parameters adaption method(RLAM) is developed to enhance
PSO in convergence by designing a network to control the coefficients of PSO.
Moreover, based on RLAM, a new RLPSO is designed.
  In order to investigate the performance of RLAM and RLPSO, experiments on 28
CEC 2013 benchmark functions are carried out when comparing with other online
adaption method and PSO variants. The reported computational results show that
the proposed RLAM is efficient and effictive and that the the proposed RLPSO is
more superior compared with several state-of-the-art PSO variants.
