自從最適實驗設計的概念被提出後，至今已普遍地被接受並應用於各項領域中。粒子群演算法為一個次經驗演算法，簡單並具有強大的能力能解決複雜的最佳化問題。近幾年，由於粒子群演算法的潛力與優點，已被廣泛應於在各領域中也引起學界的注意。我們將此演算法應用於各種不同混合模型與羅吉斯迴歸混合模型中找出其正合最適設計並與其他結果做比較。

Particle swarm optimization (PSO) is a meta-heuristic algorithm which is simple to use but powerful to solve complicated optimization problems. In recent years, PSO has been used in many aspects and attracts people's attention due to the potential abilities and considerable advantages of the PSO algorithm. In this thesis, we apply the projection PSO algorithm to find exact D-optimal designs with respect to the different numbers of support points for different types of mixture models. In addition, we also take the projection PSO algorithm to search the exact D-optimal mixture designs for binary response based on the logistic models.

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