中文摘要：

对超快速模拟退火算法进行改进（A—VFSA），并以此为粒子发生器，提出了PSO-PG算法。该算法通过粒子发生器对搜索域内粒子进行改进，从而生成一个精英粒子池；并根据随机策略从粒子池中选择粒子，作为初始种群，采用PSO算法进行优化，得到全局最优解。相比于标准PSO算法和LDW算法，PSO—PG算法拥有更好的稳定性和优化精度，能够更加快速地收敛到全局最优解，在一定范围内几乎不依赖于初始参数的选择。

英文摘要：

This paper developed a new global optimization algorithm equipped with a particle generator, which was named by particle swarm optimization with particle generator （PSO-PG）. The algorithm used particle generator, which was designed by means of improved very fast simulated re-annealing, to produce an optimized initial swarm of particles for PSO. Based on the several numerical experiments, it is found that the PSO-PG algorithm is more stable and accurate than the standard PS0 meth- od and linear adaptive inertia weight PSO method （LDW）. Furthermore, the PSO-PG is almost independent of the selection of some critical parameters employed in the algorithm