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  本文關(guān)鍵詞： 試驗設(shè)計 全局仿真優(yōu)化 響應(yīng)面 支持向量回歸 增量法　出處：《華中科技大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：囿于傳統(tǒng)全局優(yōu)化方法及其它基于替代模型的全局仿真優(yōu)化方法存在估值次數(shù)多、無法應(yīng)對高維優(yōu)化問題等缺點，近些年開始流行基于“黑箱”的元模型（響應(yīng)面）方法，，主要包括基于SVR、基于RSM、基于Kriging、基于RBF等元模型的全局優(yōu)化方法。該方法是以試驗設(shè)計與數(shù)理統(tǒng)計為基礎(chǔ)的函數(shù)逼近類全局優(yōu)化方法，可通過較少的試驗在設(shè)計變量和設(shè)計目標之間獲得一個足夠準確的函數(shù)關(guān)系，利用響應(yīng)面替代模型有效降低了優(yōu)化問題的計算成本。 支撐向量回歸（SVR，Support Vector Regression）基于SVM理論，通過獲得訓(xùn)練樣本的最大間隔建立分類超平面，以構(gòu)造源模型的替代模型響應(yīng)面。目前存在的基于SVR的全局仿真優(yōu)化方法無法保證樣本數(shù)較少時，遴選出具有代表性的樣本，使之覆蓋整個設(shè)計區(qū)間；重構(gòu)SVR模型時間較長；最優(yōu)點搜索速度較慢；不能有效應(yīng)對約束條件下的全局尋優(yōu)。 本文提出一種基于增量SVR模型的全局優(yōu)化算法DISVR：采用一種新的最小距離最大化增量LHD采樣方法，以確保樣本集分布均勻；利用支持向量機理論中支持向量集與訓(xùn)練樣本集之間存在等價關(guān)系的自身特點，構(gòu)建一種增量SVR算法重構(gòu)響應(yīng)面，以快速優(yōu)化大批量采樣點；采用加約束的DIRECT算法作為搜索策略對結(jié)構(gòu)模型進行求解，以有效解決帶約束優(yōu)化問題。 本文對于DISVR算法各重要環(huán)節(jié)均有相關(guān)章節(jié)鋪述，標準函數(shù)及工程實例的測試結(jié)果表明，本文提出的DISVR算法可高效穩(wěn)定地得到優(yōu)化結(jié)果，具有良好的應(yīng)用前景。
[Abstract]:Due to the disadvantages of traditional global optimization methods and other global simulation optimization methods based on alternative models, there are many disadvantages such as the number of estimates and the inability to cope with high-dimensional optimization problems. In recent years, the meta-model (response surface) method based on "black box" has become popular, including SVR-based, RSM-based and Kriging based. The global optimization method based on RBF iso-element model, which is based on experimental design and mathematical statistics, is a functional approximation class global optimization method. A sufficiently accurate functional relationship between design variables and design objectives can be obtained by fewer experiments, and the computational cost of the optimization problem can be effectively reduced by using the response surface substitution model. Support Vector regression support Vector Regression is based on the SVM theory and establishes the classification hyperplane by obtaining the maximum interval of training samples. The existing global simulation optimization method based on SVR can not guarantee that the representative samples can be selected to cover the whole design interval when the number of samples is small. The reconstruction time of SVR model is longer; The best search speed is slow; It can not effectively deal with the global optimization under constraint conditions. A global optimization algorithm based on incremental SVR model is presented in this paper. A new minimum distance maximization incremental LHD sampling method is adopted to ensure the uniform distribution of the sample set. Taking advantage of the equivalent relationship between the support vector set and the training sample set in the support vector machine theory, an incremental SVR algorithm is constructed to reconstruct the response surface in order to quickly optimize the mass sampling points. The constrained DIRECT algorithm is used as a search strategy to solve the structural model effectively. The test results of standard function and engineering examples show that the proposed DISVR algorithm can get the optimization results efficiently and stably. It has good application prospect.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH122

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本文編號：1453678