本論文之目標在發展一桁架最佳化的方法，來最少化桁架重量及其使用斷面型式的數量。文中桁架最佳化設計採用力法及遺傳演算法。以斷面積及節點座標為設計變數。在改良式的遺傳演算法中，同時導入了無參數化適應懲罰策略的懲罰函數法及減少設計變數字串長度的設計空間窄化技術。其拘束條件包括元件應力、細長比、節點位移及挫曲應力。除此，考量到結構及其負荷皆具有其不確定性，本文將桁架的參數、負荷與強度視為隨機變數，採用可靠度拘束進行最佳化。文中列舉了數個範例，並與過去文獻作比較。在確定系統，其結果相當接近甚至更佳。在隨機系統，結果顯示最佳化重量隨著隨機變數之變異係數與要求之可靠度增加而遞增。

The aim of this thesis is to develop a method to optimize trusses in minimum weight and number of cross-section types used. The optimization of trusses is performed using the force method and genetic method. The cross-section areas and nodal coordinates are taken into account as design variables. In the modified genetic algorithm, both the penalty function of parameter-less adaptive penalty scheme and the narrowing design space technology by contraction of the length of strings are employed simultaneously. The constraints consist of member stresses, slenderness ratios, nodal displacements and buckling stresses. In addition considering the inherent uncertainties on structures and their loads, the truss parameters, loads and strengths are also considered as random variables in the study. Accordingly the optimization is proceeded using reliability based constraints. In the thesis several numerical examples are illustrated and their results are compared with the reference papers. In the case of deterministic system, they are much closed and the present results are even better. In the case of random system, it shows that the optimum weight increases with both the coefficient of variation of random variable and the required reliability.

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