Enhanced Particle Swarm Optimization via Reinforcement Learning

Particle swarm optimization (PSO) method is a well-known optimization algorithm, which shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this paper, a reinforcement learning method is used to enhance PSO in convergence by replacing the uniformly distributed random number in the updating function by a random number generated from a well-selected normal distribution. The mean and variance of the normal distribution are estimated from the current state of each individual through a policy net. The historic behavior of the swarm group is learned to update the policy net and guide the selection of parameters of the normal distribution. The proposed algorithm is tested with numerical test functions and the results show that the convergence rate of PSO can be improved with the proposed Reinforcement Learning method (RL-PSO).