Parameter setting and experimental analysis of the random number in particle swarm optimization algorithm
粒子群算法中随机数参数的设置与实验分析

The parameters in particle swarm optimization have important effect on the optimization performance. The parameter setting of the random number in the particle swarm optimization model is analyzed by the experiments. First, because of different structures in different high-level languages, we find that in the program of particle swarm optimization algorithm, different components of a velocity vector may have different parameter settings for the corresponding random number in the particle velocity updating equation. Next, in continuous function optimization and benchmark tests of Job Shop scheduling, as well as the computation of the equipment-possession-quantity parameter optimization model, all results indicate that different parameter settings for the random number may cause significantly different effects on the optimization performance of particle swarm optimization algorithm. Furthermore, it is also found that the optimization efficiency of a particle swarm optimization algorithm can be obviously improved if the corresponding random number of different components of a velocity vector is set to the same value.