Research on Particle Swarm Algorithm Based on Adaptive Acceleration Mechanism

Abstract

Aiming at the problems of the particle swarm optimization algorithm, such as poor parameter adjustment ability, low convergence accuracy and easy to fall into local minimum, this paper proposes a particle swarm optimization algorithm based on adaptive acceleration mechanism. According to the current particle position priority, the algorithm adjusts the particle flight acceleration in real time, so that the particles jump out of the local optimal position trap and avoid premature phenomenon. Take some tests about the adaptive acceleration mechanism, convergence accuracy, anti-interference ability and particle diversity of the proposed algorithm. The experimental results show that the particle swarm optimization algorithm with adaptive acceleration mechanism not only enhances the local and global search ability, but also improves the convergence accuracy, convergence speed and avoids the premature problem.