本論文提出以MOEA/D 為基礎結合區域搜尋的演算法，並針對演算法各階段
進行實驗分析，在MOEA/D 方面對效能影響最大者為取代機制，我們發現使用學
者提出的DU 取代機制搭配 Tchebycheff 方法效果最好；而區域搜尋能讓效能大幅
提升；我們也針對於區域搜尋的各階段進行實驗分析，發現適應性控制的效果並
不顯著，這部分為我們將來深入研究的目標。除此之外，在族群初始化階段加入
策略解對於效能也有明顯提升。最後我們將演算法與已知最佳解進行效能比較；
實驗結果顯示我們的演算法在中、大型測資效能表現良好。
未來可能的研究方向，可以加入新的擾動機制，讓個體脫離區域最佳解；除
此之外尚可研究針對具有問題背景知識（domain knowledge）的搜尋方式進行適
應式控制。在選擇機制方面可以排除重複的個體，增加族群多樣性。另一方面，
可以將MOEA/D-LS 用於求解不同目標，如最大延遲時間等。
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