在日常生活中，我們時常面臨最佳化問題，例如最小化交通的時間與成本，這兩項目標存在著衝突，此類問題稱為多目標最佳化問題，因應每人需求不同，會有不同的最佳解。一般解多目標最佳化問題是找出一組最佳解集合，集合中會有不同的目標取捨方式供使用者挑選，然而解決此類問題是相當耗時的，為了在有效時間內找出不錯的解，使用演化式演算法是廣受好評的方式。
演化式演算法本身存在著許多可切割平行的要素，因此許多平行架構的演化式演算法因應而生，本論文嘗試將知名的多目標演化式演算法 MOEA/D 進行平行化，除了基本的平行要素外，尚有其他因平行化被破壞的 MOEA/D 之要素需要修補。本論文針對17個多目標最佳化問題進行測試，並慢慢調整島嶼式 MOEA/D，一一討論各要素之影響，最後與 MOEA/D 進行比較成效與差異性，並且運用 OpenMP 進行平行加速。

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