基于粒子群算法和支持向量機的船舶結構優(yōu)化
發(fā)布時間：2019-01-30 21:31
【摘要】：船舶結構優(yōu)化是船舶設計的重要方面，其主要目的在于以尋求優(yōu)化的結構形式。由于船舶結構優(yōu)化過程中涉及的設計變量數(shù)目眾多、種類多樣、所受的約束條件復雜，這導致了目標函數(shù)的非線性程度強，很難尋求到優(yōu)化問題的最優(yōu)解。需選擇合適的優(yōu)化算法進行結構優(yōu)化，粒子群算法作為一種新型的智能算法，可實現(xiàn)性強，收斂性好，有優(yōu)秀的全局搜索能力。本文將粒子群算法應用于優(yōu)化問題之中，先以三個經(jīng)典的桁架結構驗證了粒子群算法由于結構優(yōu)化的有效性，在此基礎之上提出MATLAB粒子群算法工具箱和有限元程序相結合應用于結構優(yōu)化的技術路徑，建立了一三艙段結構有限元模型，將上述優(yōu)化路徑用于結構優(yōu)化，，得到良好的優(yōu)化結果，驗證單目標粒子群算法應用于船舶結構優(yōu)化的可行性。 船舶結構優(yōu)化過程中，往往需要調(diào)用有限元軟件進行迭代計算，以獲得結構響應作為優(yōu)化過程中的目標函數(shù)或者約束條件，而因為船舶結構優(yōu)化的復雜性，迭代次數(shù)會較大，這使得船舶結構優(yōu)化過程需要消耗比較大的時間成本。在優(yōu)化過程之中借助近似模型可以減少優(yōu)化所需的時間成本，提升優(yōu)化效率，支持向量機作為一種有效的近似模型，可以對各種復雜非線性問題進行回歸。在結構優(yōu)化問題中，其可以用來建立結構響應近似模型，以預測結構響應、代替復雜費時的有限元計算。支持向量機的參數(shù)選取是支持向量機應用的難點之一，一般的基于經(jīng)驗的方法很難尋求到適合特定問題的支持向量機參數(shù)，本文將支持向量機參數(shù)的選取抽象為一優(yōu)化問題，建立了優(yōu)化的方法尋求支持向量機參數(shù)的方法，為支持向量機參數(shù)的選取找到了一條切實可行的路徑，利用粒子群算法選取支持向量機的參數(shù)，得到了具有最優(yōu)參數(shù)的支持向量機近似模型，并與基于經(jīng)驗參數(shù)的支持向量機做了對比，以驗證本文所提的參數(shù)選取方法的有效性。 在近似模型的參數(shù)選卻基礎之上，本文提出了基于支持向量機和粒子群算話的結構優(yōu)化方法，在支持向量機參數(shù)選取方法的基礎之上，建立支持向量機近似模型，并與粒子群算法相結合，用于結構優(yōu)化，為驗證上述方法的有效性，利用上述方法對船舶結構進行優(yōu)化。
[Abstract]:Ship structure optimization is an important aspect of ship design. Due to the large number of design variables involved in the process of ship structure optimization, the variety of design variables and the complexity of constraints, this leads to a strong degree of nonlinearity of the objective function, and it is difficult to find the optimal solution of the optimization problem. As a new kind of intelligent algorithm, particle swarm optimization has the advantages of strong realizability, good convergence and excellent global searching ability. In this paper, particle swarm optimization (PSO) algorithm is applied to the optimization problem. Firstly, three classical truss structures are used to verify the effectiveness of PSO due to structural optimization. On the basis of this, the technical path of the combination of MATLAB particle swarm optimization toolbox and finite element program for structural optimization is proposed, and the structural finite element model of the first and third cabins is established, and the above optimized path is applied to the structural optimization. Good optimization results are obtained, and the feasibility of applying single objective particle swarm optimization algorithm to ship structure optimization is verified. In the process of ship structure optimization, it is often necessary to use finite element software for iterative calculation to obtain structural response as the objective function or constraint condition in the optimization process. However, because of the complexity of ship structure optimization, the iteration times will be larger. This makes the ship structure optimization process requires a relatively large time cost. In the process of optimization, the time cost of optimization can be reduced and the optimization efficiency can be improved by using approximate model. As an effective approximate model, support vector machine (SVM) can be used to regress various complex nonlinear problems. In the structural optimization problem, it can be used to establish the approximate model of the structural response to predict the structural response, instead of the complicated and time-consuming finite element calculation. The parameter selection of support vector machine is one of the difficulties in the application of support vector machine. It is difficult to find support vector machine parameters suitable for a specific problem in general experience-based methods. In this paper, the selection of support vector machine parameters is abstracted as an optimization problem. The optimization method is established to find the parameters of support vector machine. A feasible path is found for the selection of support vector machine parameters. The particle swarm optimization algorithm is used to select the parameters of support vector machine. The approximate model of support vector machine with optimal parameters is obtained and compared with that of support vector machine based on empirical parameters to verify the effectiveness of the proposed parameter selection method. On the basis of parameter selection of approximate model, a structure optimization method based on support vector machine (SVM) and particle swarm optimization (PSO) is proposed in this paper. On the basis of parameter selection method of support vector machine (SVM), an approximate model of support vector machine (SVM) is established. The method is combined with particle swarm optimization algorithm to optimize the structure of ships. To verify the effectiveness of the above methods, the above methods are used to optimize the ship structure.
【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U662;TP18
本文編號：2418545
[Abstract]:Ship structure optimization is an important aspect of ship design. Due to the large number of design variables involved in the process of ship structure optimization, the variety of design variables and the complexity of constraints, this leads to a strong degree of nonlinearity of the objective function, and it is difficult to find the optimal solution of the optimization problem. As a new kind of intelligent algorithm, particle swarm optimization has the advantages of strong realizability, good convergence and excellent global searching ability. In this paper, particle swarm optimization (PSO) algorithm is applied to the optimization problem. Firstly, three classical truss structures are used to verify the effectiveness of PSO due to structural optimization. On the basis of this, the technical path of the combination of MATLAB particle swarm optimization toolbox and finite element program for structural optimization is proposed, and the structural finite element model of the first and third cabins is established, and the above optimized path is applied to the structural optimization. Good optimization results are obtained, and the feasibility of applying single objective particle swarm optimization algorithm to ship structure optimization is verified. In the process of ship structure optimization, it is often necessary to use finite element software for iterative calculation to obtain structural response as the objective function or constraint condition in the optimization process. However, because of the complexity of ship structure optimization, the iteration times will be larger. This makes the ship structure optimization process requires a relatively large time cost. In the process of optimization, the time cost of optimization can be reduced and the optimization efficiency can be improved by using approximate model. As an effective approximate model, support vector machine (SVM) can be used to regress various complex nonlinear problems. In the structural optimization problem, it can be used to establish the approximate model of the structural response to predict the structural response, instead of the complicated and time-consuming finite element calculation. The parameter selection of support vector machine is one of the difficulties in the application of support vector machine. It is difficult to find support vector machine parameters suitable for a specific problem in general experience-based methods. In this paper, the selection of support vector machine parameters is abstracted as an optimization problem. The optimization method is established to find the parameters of support vector machine. A feasible path is found for the selection of support vector machine parameters. The particle swarm optimization algorithm is used to select the parameters of support vector machine. The approximate model of support vector machine with optimal parameters is obtained and compared with that of support vector machine based on empirical parameters to verify the effectiveness of the proposed parameter selection method. On the basis of parameter selection of approximate model, a structure optimization method based on support vector machine (SVM) and particle swarm optimization (PSO) is proposed in this paper. On the basis of parameter selection method of support vector machine (SVM), an approximate model of support vector machine (SVM) is established. The method is combined with particle swarm optimization algorithm to optimize the structure of ships. To verify the effectiveness of the above methods, the above methods are used to optimize the ship structure.
【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U662;TP18
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