隨著時代的進步，衍生出許多由觀察自然界生物的習性而產生的演算法，而粒子群演算法(Particle Swarm Optimization,PSO)，就是這麼產生的，在本文內主要是討論如何將PSO演算法，應用在常見具隨機效應的迴歸模型及潛在狄利克雷分配(Latent Dirichlet Allocation,LDA)，由我們的演算法可看出，雖然在計算過程的部分能簡化很多，但由於粒子數增加相對的所要花費的時間也會長一些。

With the progress of the eras, many algorithms generated by the observation of natural biological habits, so does Particle Swarm Optimization. In this paper, we discuss how the PSO algorithm used in common regression models with random effects and Latent Dirichlet Allocation. By our algorithm, it can be seen that even though the part of the calculation process can be simplified a lot, but spend more time because of relatively increase number of particles.

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