摘要

The searching process of particle swarm optimizations (PSO) includes four states: exploration, exploitation, convergence and jump-out. Performance information of each state is essential to learn the characteristics of different algorithms as well as to improve their performances. To this end, this paper discusses a novel performance evaluation method of each phase in PSOs. Firstly, we propose a velocity-based state estimation (VSE) method, which can estimate the real-time state of PSO variants with less computation. Subsequently, we provide a phase performance evaluation based on VSE, which includes phase identification, two kinds of phase performance indicators and ranking method. Finally, we design hybrid algorithm experiments, to compare phase performance of six main PSO algorithms, and the phase replacement experiments is used to verify the experimental results.

  • 出版日期2017-8
  • 单位中国人民解放军陆军工程大学