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工程结构优化设计的新方法 总被引:13,自引:1,他引:12
阐述了遗传算法求解工程结构非线性优化问题的方法 ,实例计算表明 ,具有全局优化和并行计算特点的遗传算法是求解工程结构非线性优化问题可行有效的方法。 相似文献
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结构拓扑优化对于建筑创新设计而言有着重要意义,设计师通常会利用不同的数值模型实现优化目标,本文对纯力学找形优化、遗传算法优化等各种优化计算策略的模型特征和使用边界进行了探讨,通过相关案例分析了各优化模型的优点及可能存在的局限性。 相似文献
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遗传算法是伴随着神经网络理论,近几年在国内外广泛兴起的一种新型优化算法。本文首镒应用遗传算法,对供热系统的多热源选址进行优化计算,取得了满意结果。 相似文献
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综合性遗传算法用于水质模型参数估值 总被引:4,自引:0,他引:4
将一种具有更高收敛速度、更少迭代次数的综合性遗传算法应用于水环境模型参数估值之中,通过与简单遗传算法计算结果的对比验证了新方法的有效性。 相似文献
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提出了一种基于遗传算法的时域内载荷识别方法,并通过工程实例加以分析,将遗传算法应用到载荷识别过程中,将结构动力学的反问题转化为正计算,避免了传统方法的不足,进一步证明了该算法的有效性。 相似文献
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《四川建筑》2021,41(1)
斜塔斜拉桥因其独特的结构特点和美学特征常被应用于城市建设中;它通过将桥塔倾斜,利用其不平衡力矩改善整体结构的受力性能,但其桥塔和主梁的受力情况更为复杂,确定合理的成桥状态索力以改善结构受力显得尤为重要。文章基于遗传算法,以某有背索斜塔斜拉桥为例,首先应用Midas有限元软件建立全桥梁单元模型,并运用未知荷载系数法求解出一组成桥状态索力较优解,用以生成遗传算法初始种群;并结合python编程语言对有限元软件ABAQUS进行了二次开发,以结构弯曲应变能最小化为目标,使其能够在不同初始索力条件下求解并计算结构弯曲应变能,以遗传算法为索力更新策略多次迭代求解计算,最终得到一组最优索力解。同时,为解决在种群基数较大情况下求解效率低下问题,通过引入精英保留策略和优质初始化种群策略(简称双策略)进行算法改进,提高了收敛效率。计算结果表明,相对于一次落架状态,优化后主梁和桥塔最大弯矩分别降低87.9%和67.8%,主梁竖向位移和桥塔纵向位移分别降低90.1%和96.3%,由此可见遗传算法的有效性。 相似文献
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工程实用的遗传算法结构优化设计 总被引:1,自引:0,他引:1
针对工程结构设计的特点以及目前遗传算法在大规模设计变量设计中的计算量大、优化结果较差的特点,提出了相应的改进措施,算例表明改进方法是有效的。 相似文献
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主要介绍了一种基于非连续设计变量的结构优化设计方法一遗传算法(Genetic Al-gorithms,GA)。首先对遗传算法的来源、基本过程进行了论述;为了提高遗传算法的收敛性能,同时考虑到交叉率和变异率的选取问题,引入一种基于个体适应度值的自适应调整交叉率和变异率的自适应遗传算法,并通过算例表明自适应遗传算法是有效的。 相似文献
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While it is possible to check the energy performance of a given building by means of several available methods, the inverse problem of determining the optimum configuration given a desired performance is more difficult to solve. In the Mediterranean region this problem is more complex due to the following two reasons: the air-conditioning load is as important as the heating load, and the energy needs depend on a high number of architectural parameters which have different, even contradictory, effects on summer and winter loads. In this paper we present an optimization algorithm that couples pseudo-random optimization techniques, the genetic algorithms (GA), with a simplified tool for building thermal evaluation (CHEOPS) for the purpose of minimizing the energy consumption of Mediterranean buildings. Since increasing the energy performance usually requires the use of special devices resulting in a high construction cost, we also propose to use GA for the purpose of economical optimization. 相似文献
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Genetic evolutionary structural optimization 总被引:1,自引:0,他引:1
Evolutionary structural optimization (ESO) is based on a simple idea that an optimal structure (with maximum stiffness but minimum weight) can be achieved by gradually removing ineffectively used materials from design domain. In general, the results from ESO are likely to be local optimums other than the global optimum desired. In this paper, the genetic algorithm (GA) is integrated with ESO to form a new algorithm called Genetic Evolutionary Structural Optimization (GESO), which takes the advantage of the excellent behavior of the GA in searching for global optimums. For the developed GESO method, each element in finite element analysis is an individual and has its own fitness value according to the magnitude of its sensitivity number. Then, all elements in an initial domain constitute a whole population in GA. After a number of generations, undeleted elements will converge to the optimal result that will be more likely to be a global optimum than that of ESO. To avoid missing the optimum layout of a structure in the evolution, an interim thickness is introduced into GESO and its validity is demonstrated by an example. A stiffness optimization with weight constraints and a weight optimization with displacement constraints are studied as numerical examples to investigate the effectiveness of GESO by comparison with the performance of ESO. It is shown through the examples that the developed GESO method has powerful capacity in searching for global optimal results and requires less computational effort than ESO and other existing methods. 相似文献
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针对遗传算法在离散变量结构优化设计中的缺陷,将进退搜索算法同遗传算法相结合,提出了一种混合遗传算法。建立了离散变量结构优化模型,并对一11杆桁架结构进行了优化设计。算例结果表明,混合遗传算法收敛快、精度高,应用于离散变量结构优化设计是有效的。 相似文献
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Naima Hamid Rachida Abounacer Mohammed Idali Oumhand M’barek Feddaoui Driss Agliz 《国际自然能源杂志》2019,40(5):517-524
To design a high-performance photovoltaic (PV) system, the parameters extraction of solar cell models is exceedingly crucial. A new variant of the genetic algorithm (GA) called Genetic Algorithm with Convex Combination Crossover (GACCC) is proposed to identify the unknown electrical parameters of different solar cell models, i.e. single diode, double diode, and PV module. GACCC is achieved by integrating a new crossover operation to maintain a good balance between the intensification of the best solutions and the diversification of the search space. To test the proposed GACCC, we have compared it to the basic GA and with other literature techniques. The results indicate a high performance of developed approach GACCC and a high accuracy of estimated parameters. In addition, the efficiency of the results is confirmed by the good agreement between the experimental I-V data and the simulated results in all cases. 相似文献
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混合遗传算法在桁架优化中的应用 总被引:3,自引:0,他引:3
应用遗传算法,并对基本遗传算法进行相应的改进,对空间桁架结构截面进行优化。在应用遗传算法的同时,考虑满应力解通常处在最优解附近的原理,将满应力解作为种群中的一个个体参与计算,并通过MATLAB编制相应的程序实现。算例表明,该方法能得到理想效果,并减少程序运算时间。 相似文献