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 strategy is developed to enhance PSO in convergence by replacing the uniformly distributed random number in the updating function with a random number generated from a selected normal distribution. In the proposed method, the mean and standard deviation of the normal distribution are estimated from the current state of each individual through a policy net. The historic behaviour of the swarm group is used to update the policy net and guide the selection of parameters of the normal distribution. The proposed method is integrated into the original PSO and a state-of-the-art PSO, called the self-adaptive dynamic multi-swarm PSO (sDMS-PSO), and tested with numerical functions and engineering problems. The test results show that the convergence rate of PSO methods can be improved with the proposed reinforcement learning strategy.

粒子群优化 (PSO) 是一种众所周知的优化算法，在解决不同的优化问题方面表现出良好的性能。然而，PSO 通常会遇到收敛缓慢的问题。在本文中，开发了一种强化学习策略，通过将更新函数中的均匀分布随机数替换为从选定的正态分布生成的随机数来增强 PSO 的收敛性。在所提出的方法中，正态分布的均值和标准差是通过策略网络从每个个体的当前状态估计的。群体的历史行为用于更新策略网络并指导正态分布参数的选择。所提出的方法被集成到原始 PSO 和最先进的 PSO 中，称为自适应动态多群PSO（sDMS-PSO），并用数值函数和工程问题进行了测试。测试结果表明，提出的强化学习策略可以提高 PSO 方法的收敛速度。