论文标题
基于强化学习的参数适应方法用于粒子群优化
Reinforcement learning based parameters adaption method for particle swarm optimization
论文作者
论文摘要
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显示出良好的性能。但是，PSO通常患有缓慢的收敛性。在本文中，开发了基于增强学习的在线参数适应方法（RLAM），以通过设计网络来控制PSO系数，从而增强PSO的融合。此外，根据Rlam，设计了新的RLPSO。 为了研究RLAM和RLPSO的性能，与其他在线适应方法和PSO变体进行比较时，将对28 CEC 2013的基准功能进行实验。报告的计算结果表明，所提出的Rlam具有有效和有效的作用，并且与几种最新的PSO变体相比，所提出的RLPSO更高。
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.
