MULTIOBJECTIVE OPTIMIZATION OF BAR STRUCTURES BY PARETO-GA
MULTIOBJECTIVE OPTIMIZATION OF BAR STRUCTURES BY PARETO-GA
复制标题
帕累托遗传算法对条形结构的多目标优化
DOI：
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
G. Winter
中科院分区：
文献类型：
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作者：
David Greiner;J. M. Emperador;G. Winter
这里考虑的优化问题是在多目标环境下使具有离散实截面类型的框架的重量最小化：·首先，考虑不同约束条件的结构重量最小化。·第二，最大限度地减少不同横截面类型的数量，这在满足建设性要求的大型结构中很重要。这些约束是：杆件的应力、杆件连接处或杆件中点的位移，以及包括屈曲效应在内的细长极限(如西班牙规范所述)。我们使用非支配排序遗传算法(NSGA)和格雷码相结合的精英策略来解决最小化问题。将结果与不同的简单目标遗传算法(世代、稳态或CHC)进行了比较，在不同的应用中得到的结果表明，NSGA成功地向Pareto解进化，实现了对前沿部分解的保持。精英算子的引入显著提高了所获得解的质量，并且在初始种群中包含高质量解也可以是获得改进的最终前沿D.Greiner、J.M.Emperador和G.温特的一种方式。
The optimisation problem considered here is to minimize the weight of frames with discrete real cross section types under a multiobjective context : • First, the minimisation of the weight of the structure taking into account different constraints. • Second, the minimisation of the number of different cross section types, important in large structures for constructive requirements. The constraints are : stresses of the bars, displacements of joints or middle points of bars, and slenderness limits to include the buckling effect (as described in the Spanish code). We solve the minimisation problem using an elitist strategy of the Non-dominated Sorting Genetic Algorithm (NSGA) and Gray Code. Results are compared with different simple objective GA strategies, such as generational, steady-state or CHC, and the results obtained in different applications demonstrate that the maintenance of partial solutions in the front by the NSGA is carried out with successfully evolution towards to the Pareto solution. The introduction of the elitist operator improves significantly the quality of the obtained solution and inclusion of high quality solutions in the initial population can be also a way to obtain improved final fronts D. Greiner, J.M. Emperador, and G. Winter.