Abstract
Under the environment of cloud, particle swarm algorithm is widely used in intelligent computer field. The combination model of the logistics service is solved. However, in solving workflow and call problems, the traditional algorithm consumes more time and does not meet the logistics application scenarios. In this paper, the particle swarm optimization algorithm was optimized and improved. The execution order of users was used to re arrange the algorithm. The experimental results showed the high efficiency of the algorithm and rapid computing speed. In order to solve the problem of particle swarm algorithm “premature”, a jamming algorithm was designed in this research. When the similarity of particle swarm was greater than a limit value, the particle position was updated optimally, and the local optimal solution and global optimal solution were retained. The particle swarm optimization algorithm could successfully avoid that the particle swarm optimization got into the local dead loop problem when searching for the optimal solution. It could be seen based on particle swarm optimization algorithm experimental results that the algorithm had high superiority in computational efficiency and speed.
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