【正文】 aintaining a population of solutions, GA’s can search for many Paretooptimal solutions in parallel. This characteristic makes GA’s very attractive for solving MO problems. The following two features are desired to solve MO problems successfully: 1) the solutions obtained are Paretooptimal and 2) they are uniformly sampled from the Paretooptimal set. NOMENCLATURE CAD: Computer Aided Design。 CAE). CAD and CAE systems are currently used in Parametric and Structural Optimization to find optimal topologies and shapes of given parts under certain conditions. This paper describes a general strategy to optimize the balance of a crankshaft, using CAD and CAE software integrated with Geic Algorithms (GAs) via programming in Java. An introduction to the groundings of this strategy is made among different tools used for its implementation. The analyzed crankshaft is modeled in mercial parametric 3D CAD software. CAD is used for evaluating the fitness function (the balance) and to make geometric modifications. CAE is used for evaluating dynamic restrictions (the eigenfrequencies). A Java interface is programmed to link the CAD model to the CAE software and to the geic algorithms. In order to make geometry modifications to our case study, it was decided to substitute the profile of the counterweights with splines from its original “arcshaped” design. The variation of the splined profile via control points results in an imbalance response. The imbalance of the crankshaft was defined as an independent objective function during a first approach, followed by a Pareto optimization of the imbalance from both correction planes, plus the curvature of the profile of the counterweights as restrictions for material flow during fing. The natural frequency was considered as an additional objective function during a second approach. The optimization process runs fully automated and the CAD program is on hold waiting for new set of parameters to receive and process, saving puting time, which is otherwise lost during the repeated startup of the cad application. The development of engine crankshafts is subject to a continuous evolution due to market pressures. Fast market developments push the increase of power, fuel economy, durability and reliability of bustion engines, and calls for reduction of size, weight, vibration and noise, cost, etc. Optimized engine ponents are therefore required if petitive designs must be attained. Due to this conditions, crankshafts, which are one of the most analyzed engine ponents, are required to be improved [1]. One of these improvements relies on material position, as panies that develop bustion engines have expressed their intentions to change actual nodular steel crankshafts from their engines, to fed steel crankshafts. Another important direction of improvement is the optimization of its geometrical characteristics. In particular for this paper is the imbalance, first Eigenfrequency and the feability. Analytical tools can greatly enhance the understanding of the physical phenomena associated with the mentioned characteristics and can be automated to do programmed tasks that an engineer requires for optimizing a design [2].The goals of the present research are: to construct a strategy for the development of engine crankshafts based on the integration of: CAD and CAE (Computer Aided Design amp。 MO: Multiobjective。gico de Monterrey through Grant No. CAT043 to carry out the research reported in this paper. REFERENCES [1] . Mourelatos, “A crankshaft system model for structural dynamic analysis of internal bustion engines,” Computers amp。目前 CAD 和 CAE 系統(tǒng)也用于設(shè)計，在一定條件下能夠選取最優(yōu)參數(shù)和結(jié)構(gòu)并且找到最佳的形狀。 CAD 適用于適應(yīng)度函數(shù) (平衡 )和幾何修改?；ㄦI不平衡要文件的響應(yīng)通過控制點的變化來控制。 前 言 發(fā)動機曲軸由于受到持續(xù)的發(fā)展演變市場的壓力。曲軸這一大多數(shù)分析引擎組件必須得到改善 [1]。分析工具可以大大提高對 物理現(xiàn)象的理解與提到的相關(guān)特性 , 工程師需要優(yōu)化設(shè)計編程任務(wù)可以自動完成 [2]。 形狀優(yōu)化和遺傳算法 遺傳算法（ GAS）是基于進化自然選擇的思想和遺傳學自適應(yīng)啟發(fā)式搜索算法（隨機搜索技術(shù)） [ 3 ] 。在進化形態(tài)優(yōu)化 技術(shù)對研究的興趣才剛剛開始增長，包括最有前途的領(lǐng)域 EA為基礎(chǔ)的形狀優(yōu)化的應(yīng)用程序之一：機械工程。 在這篇文章中曲軸的形狀優(yōu)化進行了討論，重點是配重 的幾何發(fā)展。為了使這種結(jié)合，有必要開發(fā)到氣體鏈接到 CAD模型和給 CAE分析的接口。這些目標函數(shù)是要優(yōu)化（最小化或最大化）的幾何形狀的變化。不像單目標優(yōu)化，要解決這個問題不是一個單一的點 ，而是一個家族被稱為 Pareto最優(yōu)的點集。健身功能僅適用于曲軸的計算機模型的形式，而不是解析形式。傳統(tǒng)上， GA的使用二進制數(shù)字來表示這樣的字符串：字符串具有有限的長度和字符串的每一位可以是 0或 1，通過維護解決方案的人口，遺傳算法的可搜索的并行多帕累托最優(yōu)的解決方案。氣：遺傳算法 。 CW：配重 。在一個恒定的角速度旋轉(zhuǎn)曲軸轉(zhuǎn)動 ,它的質(zhì)量差異產(chǎn)生的時刻的總和等于倍位置彎矩作用在曲軸上。同樣 ,時刻被正確的校正飛機發(fā)現(xiàn)左邊的不平衡 : llxymgrgymLrL y??)21( （ 3） llx zm gr gzm L r L z ?? )21( （ 4） 計算不平衡所需的質(zhì)量 (毫克、 rgy rgz 和慣性產(chǎn)品 Ixy Ixz)的曲軸模型可以從參數(shù)化 CAD 中獲得先進的計算數(shù)據(jù) ,它具有特殊的命令模塊，作為計算不平衡響應(yīng)評估。該算法將盡量少的絕對偏離目標函數(shù)?？梢钥闯鰞烧叩牟黄胶庑Ｕw機 ,即使是在修正區(qū)域 ,它不是接近 400 gcm定義為目標。 JAVA 的接口程可以控制 y 坐標的點 ,定義了花鍵的曲軸參數(shù)化。應(yīng)該指出的是本來預(yù)期達到目標的平衡 ,圖中沒有顯示定義邊界。在這些照片的資料砝碼接近原始設(shè)計的幾何限制。增加了一個額外的目標函數(shù) : 根據(jù)測試文件測量資料的運用描繪出所有曲線的曲率的。在這第一個方法中 ,遺傳算法是將用于生產(chǎn)自動替代曲軸形狀有限元模擬程序 ,運行模擬器 ,最后評估抗衡的形狀的基礎(chǔ)上有限元的輸 出數(shù)據(jù)。如第二部分所述進一步與 CAE 軟件的集成 , 。 提出設(shè)計以外的初始限制需要合理形狀為了不影響建立性能這明顯變得很重要，本文定義的基礎(chǔ)和戰(zhàn)略發(fā)展中曲軸的開始 ,包括可制造性和功能編譯整個系統(tǒng)多目標優(yōu)化