本研究結合基因演算法及糢糊理論的歸屬函數,提出一多目標基因演算
法,來搜尋濾波器在設計過程中最佳參數之組合.在濾波器的設計過程中,
通常使用Gradient,Quasi-Newton及Random等方法來搜尋最佳電子元件參
數的組合.然而,利用這幾種搜尋方法所找出來的參數組合常無法滿足微波
濾波器之多項規格,且容易陷入局部最佳解.此外,上述方法的搜尋結果易
受搜尋起始解的影響且需對各項規格的績效指標分別給予不同的權重,利
用加權平均法將之整合成單一指標才可應用. 為了改善上述缺失,本研究
利用糢糊理論的歸屬函數,藉由最大最小法(maxmin),將多目標的問題轉為
單一目標的問題,來解決不同的品質特性和單位不同之績效,給予權重的難
題.經由本論文所提之多目標基因演算法可找到完全符合規格的參數組合,
且此法不受起始解及搜尋範圍設定之影響,較以往之方法更具穩健性.

This thesis presents a multiple objective genetic algorithm
for finding a set of near-optimum component parameters in the
design of a microwave filter.In giving the component parameters
of a microwave filter,most previous works model this prolem as a
space-search problem and apply three major approaches,known
asthe Gradient,the Quasi-Newton,and the Random,in order to find
an optimum setting from the solution space. These three
approaches are deficient in three aspects. First, the proposed
solution may be trapped to a local optimum point before the
global optimum setting is found. Second, for a design to meet
multiple objectives, it is difficult to give appropriate weight
to each objective, and therefore the proposed solution may not
meet all the criteria. Third,the proposed solution is dependent
on the initial settng for space search. To overcome the above
three issues,this research proposes a genetic algorithm
including a novel membership function and applies the max-min
operator to convert the multiple objectives space search problem
into a single objective one. A large number of experiments has
been performed and shown satisfactoryresults.