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Design of space trusses using modified teaching-learning based optimization

机译:基于改进的教学优化的空间桁架设计

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A modified teaching-learning-based optimization (TLBO) algorithm is applied to fixed geometry space trusses with discrete and continuous design variables. Designs generated by the modified TLBO algorithm are compared with other popular evolutionary optimization methods. In all cases, the objective function is the total weight of the structure subjected to strength and displacement limitations. Designs are evaluated for fitness based on their penalized structural weight, which represents the actual truss weight and the degree to which the design constraints are violated. TLBO is conceptually modeled on the two types of pedagogy within a classroom: class-level learning from a teacher and individual learning between students. TLBO uses a relatively simple algorithm with no intrinsic parameters controlling its performance and can easily handle a mixture of both continuous and discrete design variables. Without introducing any additional algorithmic parameters, the modified TLBO algorithm uses a fitness-based weighted mean in the teaching phase and a refined student updating process. The computational performance of TLBO designs for several benchmark space truss structures is presented and compared with classical and evolutionary optimization methods. Optimization results indicate that the modified TLBO algorithm can generate improved designs when compared to other population-based techniques and in some cases improve the overall computational efficiency.
机译:改进的基于教学学习的优化(TLBO)算法应用于具有离散和连续设计变量的固定几何空间桁架。修改后的TLBO算法生成的设计与其他流行的进化优化方法进行了比较。在所有情况下,目标函数都是受强度和位移限制的结构的总重量。根据设计的适合程度,根据其受罚的结构重量评估其适用性,该重量代表实际的桁架重量和违反设计约束的程度。 TLBO在概念上以教室中的两种教学法为模型:老师的课堂学习和学生之间的个体学习。 TLBO使用相对简单的算法,没有内部参数来控制其性能,并且可以轻松地处理连续和离散设计变量的混合问题。在不引入任何其他算法参数的情况下,改进的TLBO算法在教学阶段和改进的学生更新过程中使用了基于适合度的加权平均值。给出了几种基准空间桁架结构的TLBO设计的计算性能,并与经典和进化优化方法进行了比较。优化结果表明,与其他基于人群的技术相比,改进的TLBO算法可以生成改进的设计,并且在某些情况下可以提高总体计算效率。

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