現實生活中的決策問題如投資股票時需要考慮多個目標 (風險和收益)，而這些目標通常是互相衝突的，多目標最佳化問題就是要找出同時最佳化這些目標的解集合。求解多目標問題相當困難且耗費時間，而演化式演算法 (evolutionary algorithm) 利用族群演化的特性能在單一回合就能找出近似最佳解集合，因此非常適合求解多目標問題。現今已有非常多成功的應用，但為了在求解各種不同問題時都能有良好的效能，通常需要對演算法參數進行調校，如何減少使用者調校參數的負擔，是一個十分重要的課題。
本論文針對MOEA/D-AMS 演算法中的重要參數進行動態調整，差分演化算子(differential evolution operator)的控制參數 F 和 CR 會影響子代和親代的距離和方向，本論文所使用的方法是收集演化過程中成功產生優於親代的子代所使用的參數組合，基於這些參數組合來調整往後演化所使用的參數，目的是希望讓演算法在面對不同問題的狀態時，都依然能有良好的機率產生優於親代的子代，最後實驗結果會針對演算法在17個多目標問題的效能做評比，以及具動態參數調整的演算法在處理不同型態問題時的分析和討論。

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