基于粒子群算法和支持向量机的船舶结构优化

发布时间：2019-01-30 21:31

【摘要】：船舶结构优化是船舶设计的重要方面，其主要目的在于以寻求优化的结构形式。由于船舶结构优化过程中涉及的设计变量数目众多、种类多样、所受的约束条件复杂，这导致了目标函数的非线性程度强，很难寻求到优化问题的最优解。需选择合适的优化算法进行结构优化，粒子群算法作为一种新型的智能算法，可实现性强，收敛性好，有优秀的全局搜索能力。本文将粒子群算法应用于优化问题之中，先以三个经典的桁架结构验证了粒子群算法由于结构优化的有效性，在此基础之上提出MATLAB粒子群算法工具箱和有限元程序相结合应用于结构优化的技术路径，建立了一三舱段结构有限元模型，将上述优化路径用于结构优化，，得到良好的优化结果，验证单目标粒子群算法应用于船舶结构优化的可行性。船舶结构优化过程中，往往需要调用有限元软件进行迭代计算，以获得结构响应作为优化过程中的目标函数或者约束条件，而因为船舶结构优化的复杂性，迭代次数会较大，这使得船舶结构优化过程需要消耗比较大的时间成本。在优化过程之中借助近似模型可以减少优化所需的时间成本，提升优化效率，支持向量机作为一种有效的近似模型，可以对各种复杂非线性问题进行回归。在结构优化问题中，其可以用来建立结构响应近似模型，以预测结构响应、代替复杂费时的有限元计算。支持向量机的参数选取是支持向量机应用的难点之一，一般的基于经验的方法很难寻求到适合特定问题的支持向量机参数，本文将支持向量机参数的选取抽象为一优化问题，建立了优化的方法寻求支持向量机参数的方法，为支持向量机参数的选取找到了一条切实可行的路径，利用粒子群算法选取支持向量机的参数，得到了具有最优参数的支持向量机近似模型，并与基于经验参数的支持向量机做了对比，以验证本文所提的参数选取方法的有效性。在近似模型的参数选却基础之上，本文提出了基于支持向量机和粒子群算话的结构优化方法，在支持向量机参数选取方法的基础之上，建立支持向量机近似模型，并与粒子群算法相结合，用于结构优化，为验证上述方法的有效性，利用上述方法对船舶结构进行优化。
[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.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U662;TP18

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