本研究參考記憶、進化及局部搜尋灰狼演算法、增強型灰狼演算法，並對領導狼判斷進行修改，提出增強型記憶、進化及局部搜尋灰狼演算法。為了驗證提出之演算法性能，使用十一種最佳化測試函數進行比較，比較分為兩部分。第一部分為與灰狼演算法相關變體之比較，包含灰狼演算法、遞補灰狼演算法(本研究改善領導狼之判斷)、修正灰狼演算法、加權距離灰狼演算法、增強型灰狼演算法及記憶、進化及局部搜尋灰狼演算法。第二部分為與其它演算法之比較，包含布穀鳥演算法、螢火蟲演算法、修正螢火蟲演算法、鯨魚演算法及灰狼演算法。結果顯示，本研究提出之增強型記憶、進化及局部搜尋灰狼演算法在平均目標函數值、最佳目標函數值與目標函數值之標準差的結果排名均表現優異。此外，本研究並將提出之演算法應用於真空閘閥中的簧片及碟盤幾何最佳化設計，在滿足材料降伏強度和幾何邊界條件的前提下，簧片以最大化抵抗支撐塊之力量作為目標進行幾何最佳化設計，碟盤則以最大化線性區間及最小化應力作為目標進行幾何最佳化設計，最佳化過程以有限元素分析軟體ANSYS計算目標函數值。簧片最佳化之模擬結果顯示，去除應力奇異點後之應力皆小於不鏽鋼SUS301 3/4H降伏強度774.45 MPa，但抵抗支撐塊之力量相較於原始設計之厚度0.8 mm、1mm的簧片小，此原因是因為應力與力量是正相關。碟盤最佳化模擬結果之去除接觸點之應力小於Inconel 600降伏強度365.86MPa，線性區間相較原始設計及原始設計(v74)提升了200%及28.57%；去除接觸點之應力相較於前兩款原始設計降低了14.34%及0.12%。最後，對最佳化結果進行荷重位-移實驗，最佳化簧片進行荷重-位移實驗及模擬，模擬的最大荷重為191.15N，實驗的最大荷重為147.81N，誤差為22.67%。最佳化碟盤的非線性參數之模擬的最大荷重為370.48kgf，實驗的最大荷重為299.48kgf，誤差為19.16%。

This study proposes an Augmented Memory, Evolutionary Operator, and Local Search Based Grey Wolf Optimizer (AMELGWOs) based on the Memory, Evolutionary operator, and Local search based Grey Wolf Optimizer (MELGWO), as well as the Augmented Grey Wolf Optimizer (AGWO), and by modifying the decision-making process of the leading wolves. To verify the performance of the proposed algorithm, it was compared using 11 benchmark functions. The comparison is divided into two parts. The first part involves comparing with variants related to the Grey Wolf Optimizer, including the Grey Wolf Optimizer (GWO), Grey Wolf Optimizer with substitution (GWOs), the Modified Grey Wolf Optimizer (mGWO), the Weighted Distance Grey Wolf Optimizer (wdGWO), the Augmented Grey Wolf Optimizer (AGWO), and the Memory, Evolutionary operator, and Local Search based Grey Wolf Optimizer (MELGWO). The second part involves comparisons with other algorithms, including the Cuckoo Search Algorithm (CS), Firefly Algorithm (FA), Modified Firefly Algorithm (MFA), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO). The results show that the proposed Augmented Memory, Evolutionary operator, and Local Search based Grey Wolf Optimizer (AMELGWOs) performs excellently in terms of the mean fitness, the best fitness, and the standard deviation of the fitness. Additionally, this study applies the proposed algorithm to the geometric optimization design of the leaf spring and disk in vacuum gate valves. The leaf spring is optimized to maximize force, and the disk is optimized to maximize the linear region and minimize stress, both while satisfying yield strength and boundary conditions. The optimization process involves calculating the objective function values using finite element analysis software, ANSYS.

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