A Novel Group-based Swarm Optimizer for Large-Scale Optimization

In this paper, a novel group-based swarm optimizer (GSO) is proposed for large scale optimization. Inspired from the group learning behavior in human society, the hierarchical learning are involved in GSO. In the mass learning stage, GSO randomly picks three particles to form a study group and then adopts the competitive mechanism to update the members of studing group. In the hierarchical learning phase, elite particles, the better fitness value, are directly retained for the next iteration. Worst particles learn two dominant particles to further search for more promising areas in the team at the same time. Another particles utilize differential evolution to explore search space. Then, theoretically and empirically analysis of GSO are presented and the exploration abilities of proposed method are performed compared the most popular particle swarm optimizer. Further, a set of experiments on two widely used large-scale benchmark sets demonstrate that GSO achieves better performance on large scale problems than several state-of-the-art algorithms.