关键词: 分组, 并行, 粒子群, 自适应, 惯性权重

Abstract: No fundamental change in the particle velocity update for the island model parallel particle swarm optimization, a grouping parallel particle swarm optimization with adaptive inertia weight is proposed in this paper. This algorithm can adaptively choose the number of particles joining the group in an iterative process, besides, it can adjust the inertia weight of each group adaptively in accordance with the change of the optimal position. Each grouping uses multithreading technology to parallel processing and new information sharing mode is used between particles. The simulation results show that the algorithm has higher convergence speed and convergence precision.

Key words: grouping, parallel, particle swarm, adaptive, inertia weight