在本篇論文中，我們分析了兩種基於參考點的高目標最佳化演化演算法：
NSGA-III 以及 VaEA，針對此兩種演算法的不足之處進行了討論並且嘗試不同的 參考點策略進行改良。
改良的方法包括使用 IPBI 函數來改變 NSGA-III 的搜尋方向、使用不同的 參考點策略來決定 VaEA 一開始的初始族群 (參考點)，以及將 VaEA 的選擇機 制融合進 NSGA-III 當中幫助 NSGA-III 改善族群的收斂程度及分散程度。實驗 結果映證了我們嘗試的各種參考點策略能夠根據問題有效改善演算法的效能。
本研究的未來方向包括結合 IPBI 函數與其它聚合函數，使演算法的搜尋行 為不再只針對某一類型問題，另外還有研究新的參考點生成機制或是新的動態參 考點調整策略，也可嘗試將多種不同的參考點策略進行混合。
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