傳統的鋼筋混凝土設計中，許多的變數都是由工程師的經驗去給定初始值，例如柱直徑、梁深、配筋量等等。由於這些變數之間是息息相關的，而工程師的設計通常趨於保守，設計往往也未必能有效率的使各個材料間發揮其效能。
　　舉例來說，增加鋼筋量可以增加鋼筋混凝土柱子的強度，但是相對於混凝土，鋼筋的價格昂貴許多，若無適當的設計，鋼筋量一樣，柱子的強度無法相對提升，或者造成浪費。工程師在設計的時候不易在廣大的搜尋空間中找尋適合的設計參數，若用人力計算相當費時費力，因此本研究主要目的將鋼筋量、柱子直徑、混凝土強度以及鋼筋配比作為最佳化之變數，以求取最經濟且符合結構強度規定之混凝土斷面設計。研究方法為基因演算法以及粒子群演算法。
　　本研究提出之兩套演算法應用在美國AASHTO協會橋樑設計範例之橋柱以及ACI規範下之鋼筋混凝土梁最佳化結構設計;目標為取得符合設計規範且較為經濟之設計。本研究亦比較基因演算法以及粒子群演算法的求解效果。

In the traditional reinforce concrete design, a lot of initial values of parameters are set based on the engineers' experience, such as diameter of the pillar, deep of beam, amount of reinforcing bar, etc. Since the design parameters affect the performance and the engineers usually take a conservative approach, the overall design may not necessarily be optimal.

For example, increasing the reinforcing bar amount can raise strength of RC pillar. Opposite to concrete, reinforcing bars are much expensive. Without appropriate design, even using the same number of reinforcing bars can not raise the strength. It is not economical. It is not easy to find appropriate design parameters in the large search space. This study considers diameter of pillar, number of reinforcing bars, concrete’s strength and reinforcing bar strength as a parameter to calculate economical design parameters and conforms with the design rules. The proposed methods include Genetic Algorithm and Particle Swarm Optimization Algorithm.

This study is to apply two algorithms to find the optimal beam design which is based on ACI design rule, and a pillar design which is based on AASHTO design rule. The goal is to find the optimal design parameters and conforms with the design rules. This research also compares the result of two models.

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