关键词: 粒子群算法, 健康度, 自适应过滤, 懒惰粒子, 引导因子, 收敛速度

Abstract: Since the standard particle swarm optimization (PSO) algorithm converges slowly and is easy to fall into local optimization, this paper proposes the PSO algorithm with adaptive filter based on health degree (HAFPSO). Firstly, the algorithm can determine the particle's state by detecting particle health degree and mark ill particles at the same time. In order to avoid invalid search, the algorithm adapts to filter the ill particle's position and update a new position. In order to further improve the algorithm convergence speed and accuracy, the proposed algorithm adopts the guidance factor to update the global worst particle. Compared with standard particle swarm optimization and other optimization algorithms with 11 benchmark functions, the experimental results show that HAFPSO can improve convergence speed and accuracy significantly.

Key words: particle swarm optimization, health degree, adaptive filter, lazy particle, guidance factor, convergence speed