A Dual Learning Strategy-based Particle Swarm Optimization with Adaptive Velocity Control
Xiao Yang, Zonghui Cai, Shangce Gao · 2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) · 2022
Particle swarm optimization has been paid more attention in recent decades, since its simplicity and effectiveness. Numerous variants of particle swarm optimization have been proposed for solving various optimization problems in recent years. Learning strategy between particles significantly affects their search behaviour and performance. Although various novel learning strategies have been introduced into particle swarm optimization, most of them are only use one type of mechanisms. In this paper, we propose a dual learning strategy-based particle swarm optimization with adaptive velocity control. The proposed algorithm has good exploration and exploitation which is benefited from comprehensive learning and genetic learning, respectively. Furthermore, these two abilities are well balanced according to adaptive parameter and velocity control. The proposed algorithm is tested on 30 frequently used benchmark functions, and the experimental results show that it outperforms other learning strategy-based particle swarm optimization algorithms in terms of effectiveness and robustness.