IEEE Trans Cybern. 2021 Dec;51(12):6284-6293. doi: 10.1109/TCYB.2020.2968400. Epub 2021 Dec 22.
In this article, a simple yet effective method, called a two-phase learning-based swarm optimizer (TPLSO), is proposed for large-scale optimization. Inspired by the cooperative learning behavior in human society, mass learning and elite learning are involved in TPLSO. In the mass learning phase, TPLSO randomly selects three particles to form a study group and then adopts a competitive mechanism to update the members of the study group. Then, we sort all of the particles in the swarm and pick out the elite particles that have better fitness values. In the elite learning phase, the elite particles learn from each other to further search for more promising areas. The theoretical analysis of TPLSO exploration and exploitation abilities is performed and compared with several popular particle swarm optimizers. Comparative experiments on two widely used large-scale benchmark datasets demonstrate that the proposed TPLSO achieves better performance on diverse large-scale problems than several state-of-the-art algorithms.
在本文中，提出了一种简单而有效的方法，称为基于两阶段学习的群体智能优化算法（TPLSO），用于大规模优化。受人类社会合作学习行为的启发，TPLSO 中涉及了群体学习和精英学习。在群体学习阶段，TPLSO 随机选择三个粒子形成一个学习小组，然后采用竞争机制更新学习小组成员。接着，对整个群体中的粒子进行排序，并挑选出具有更好适应度值的精英粒子。在精英学习阶段，精英粒子相互学习，进一步搜索更有前途的区域。对 TPLSO 的探索和开发能力进行了理论分析，并与几种流行的粒子群优化算法进行了比较。在两个广泛使用的大规模基准数据集上的比较实验表明，所提出的 TPLSO 在各种大规模问题上的性能优于几种最先进的算法。
