Study on the Strategy of Acceleration Factor in Particle Swarm Optimization Algorithm

Concerning the problem of Particle swarm optimization for solving complex multimodal function could easily fall into premature convergence, this paper proposed an algorithm of dynamically changing acceleration factor particle swarm optimization (CAPSO). According to the motion characteristics of the particles at different stages, acceleration factor of the particle velocity update formula were constructed as a monotonically increasing function and monotonically decreasing function. By setting the dynamic adjustment factor to dynamically determine the acceleration factor expression based on the actual simulation conditions, the algorithm had a better ability to adapt. Validated through four standard test functions and compared with other similar algorithms, numerical simulation results show that by adjusting the dynamic acceleration factor, the proposed algorithm had better optimization precision and execution ability compared with other algorithms.