多目標最佳化問題是近年演化計算領域裡熱門的研究主題，我們的日常生活周遭也充滿了多目標最佳化的問題：想要吃得好又想要錢花得少、想用較少的次數搬完東西但是又不想太費力，許多事都可以用多目標最佳化的角度來思考，其中目標數更多更複雜的高目標最佳化問題在近年獲得了許多關注，如何設計出有效率並且效能良好的高目標最佳化演化演算法已經成為了近年重要的課題。
近年發表的 NSGA-III 與 VaEA 在高目標最佳化問題都有優秀的表現，本論文對這二個演算法進行分析與討論，並嘗試不同的參考點策略來進行改良：使用 IPBI 函數改變搜尋行為，使其能在參考點分布與問題前緣形狀不符合的時候仍然有能力搜尋到最佳解；改變 VaEA 演算法的初始參考點策略，使其能夠獲得更佳的極限值；將 VaEA 的動態參考點概念與環境選擇機制與 NSGA-III 結合成新的混合演算法。實驗結果也顯示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。

英文文獻
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