多目標最佳化問題在現實生活中隨處可見，像是生產排程與規劃問題，目標通常是讓生產效能最大化而耗費成本最低。此類問題的目標通常是相互衝突的，因而求解此類最佳化問題的解集合是相當困難又耗時的。演化式演算法 ( evolutionary algorithm ) 利用族群演化的特性求取 (近似) 最佳解集合，相當適合在多目標最佳化這種類型問題上使用，因此已被廣泛使用與發展。可是演化式演算法在不同的問題上需要不同的參數設定，才能獲得較佳的效能。所以如何讓使用者在參數調校的負擔減少，是一個十分重要的項目。
本論文針對 MOEA/D-AMS 演算法中的差分式演算法主要參數 F 與 CR執行動態調整，兩者分別影響子代和親代的差異程度與選擇子代的基因交配機率。本論文使用MOEA/D-AMS 收斂度評估機制作演化時期參考分類個體，佐以三種狀態參數調整機制去對應個體不同演化時期的調整。目的是希望族群中的個體能夠在不同演化時期獲得最恰當的調整方法來增進效能。最後實驗部分則會評比演算法在17個多目標問題的效能，與其他具動態參數調整機制在處理不同型態問題時的分析和討論。

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