Abstract: To resolve the problem that particle swarm optimization algorithm has low search accuracy， slow convergence speed and easy to fall into local optimization when dealing with complex optimization problems， a dynamic multi population particle swarm without velocity based on opposition-based learning and elite promotion is proposed. Firstly， based on the particle position update mode without velocity term， this algorithm dynamically divides subpopulations and adopts different evolutionary strategies. Opposition-based learning is used to broaden the search range for subgroups， ensuring population diversity and avoid particles falling into local optimization too early. Then， in order to make full use of the information of excellent particles and improve the search accuracy， the elite promotion strategy is improved to optimize the individual historical optimal particles. The differential evolution algorithm is used to update the optimal particle of the population. Finally， the performance is tested by 22 test functions proposed by CEC2006. The experimental results show that the proposed algorithm has more excellent performance in search accuracy and stability compared with other algorithms. In addition， the proposed algorithm can effectively improve the convergence speed.

Key words: particle swarm optimization algorithm, no-velocity term, dynamic partition, opposition-based learning, elite promotion, differential evolution