韓旭 姜潮 陳國棟 龍湘云

摘 要：提出基于自適應徑向基函數(shù)的多目標優(yōu)化方法。該方法通過遺傳拉丁超立方實驗設計、徑向基函數(shù)和隔代映射遺傳算法等技術，系統(tǒng)地評價代理模型。采用改進的貪婪算法挑選最后迭代步中的測試點到最終樣本空間，獲得整個設計域上的自適應徑向基函數(shù)模型。該方法被應用于車身薄壁構件耐撞性多目標優(yōu)化設計中，快速地找到了多組設計方案，較好地平衡了薄壁構碰撞過程中的吸能量和碰撞力。提出基于智能布點技術的微型多目標遺傳算法。該算法采用加強徑向基函數(shù)構建全局代理模型，再運用高效的微型多目標遺傳算法進行近似優(yōu)化。并根據(jù)優(yōu)化結果信息進行智能布點，反饋到設計空間進而不斷地更新代理模型，使實驗設計過程和近似優(yōu)化過程形成閉環(huán)的過程，提高了優(yōu)化效率。該方法被應用于某重型商用車駕駛室動態(tài)特性優(yōu)化中，獲得大量支配優(yōu)化前的設計方案使駕駛室動態(tài)特性更好并且質(zhì)量更輕。提出基于信賴域模型管理的優(yōu)化方法。該方法將在整個設計空間上的復雜優(yōu)化問題，轉化為一系列信賴域上的近似多目標優(yōu)化問題。通過每個信賴域上的優(yōu)化結果，確定信賴度和下代域的中心、半徑。進而不斷地縮放、平移信賴域，來保證獲得與真實模型一致的非支配解。該方法被應用于某車門結構優(yōu)化實際中，通過匹配關鍵部件的厚度，很好地平衡了車門的各項動靜態(tài)特性指標。結合信賴域和智能布點技術，用來處理信賴域模型管理需要多次重采樣導致效率低下的問題。通過樣本遺傳策略，遺傳落在下代信賴域空間上的樣本，減少實驗設計樣本個數(shù)從而提高效率。通過遺傳智能布點策略，根據(jù)距離比較原則從非支配解外部解集中挑選部分到信賴域空間，提高關鍵區(qū)域代理模型的精度從而加快收斂。該方法被成功應用于基于耐撞性和模態(tài)特性的轎車車身結構輕量化設計中，解決了汽車結構安全中的多目標優(yōu)化問題。

關鍵詞：汽車結構安全 多目標優(yōu)化 代理模型 智能布點 信賴域

Multi-Objective Optimization Method Based on Metamodel for Vehicle Structural Safety

Han Xu Jiang Chao Chen Guodong Long Xiangyun

（Hunan University）

Abstract：Most vehicle structural safety optimization problems involve multiple objectives， which cannot be expressed explicitly but acquired by complex computational model， and thus it increases the difficulty of solving multi-objective optimization problems. Intelligent optimization method is able to search for multiple optimal solutions in one single simulation run， but the low efficiency limits its application to complex vehicle structural crash problems. Common multi-objective optimization methods based on metamodel can well deal with the low efficiency and become a research focus， but the solution accuracy is usually low. Therefore， this project studies the multi-objective optimization methods based on metamodel， aims to improve the efficiency and accuracy in the design of vehicle crash safety. A new multi-objective optimization algorithm is proposed based on adaptive radial basis function. This method effectively assesses metamodel by using inherit Latin hypercube design， radial basis function and intergeneration projection genetic algorithm. The proposed method is applied to the thin-walled sections for structural crashworthiness， which is beneficial to quickly find multi-group design schemes and can well balance energy absorption and collision force. A micro multi-objective genetic algorithm based on intelligent sampling technology is put forward. The algorithm adopts the extented radial basis function to build a global metamodel， and then employs the efficient micro multi-objective genetic algorithm for approximate optimization. The method has been used in the dynamic characteristic optimization of a heavy commercial vehicle cab and obtains many optimal design schemes. Optimization algorithm based on trust region model management is proposed to solve the multi-objective optimization problem in complex engineering. The method transforms the complex optimization problems in the entire design space into a series of approximation problems in trust region. The method has been applied in a door structure optimization， and well balances the static and dynamic performance by matching the thickness of key components. Based on trust region and intelligent sampling technology， an efficient multi-objective method is developed. The method has been successfully used in the lightweight design of car body based on crashworthiness and modal characteristics， and demonstrates its ability to solve multi-objective optimization problems in vehicle structural safety.

Key Words：Vehicle structural safety； Multi-objective optimization； Metamodel； Intelligent sampling； Trust region

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