An adaptive diversity strategy for particle swarm optimization

In this paper, we present a diversity strategy for particle swarm optimizer. The modified algorithm re-initializes part of particles with poorer fitness during the searching process. It is empirically tested and compared with other published methods on many famous benchmark functions. The experimental results illustrate that the proposed algorithm has the potential to achieve higher success ratio and better solution quality. It is very competitive for hard multimodal function optimization.