It is an undeniable fact that there are main challenges in the use of metaheuristics for optimal design of real-size steel frames in practice. In general, steel frame optimization problems usually require an inordinate amount of processing time where the main portion of computational effort is devoted to myriad structural response computations during the optimization iterations. Moreover, the inherent complexity of steel frame optimization problems may result in poor performance of even contemporary or advanced metaheuristics. Beside the challenging nature of such problems, significant difference in geometrical properties of two adjacent steel sections in a list of available profiles can also mislead the optimization algorithm and may result in trapping the algorithm in a poor local optimum. Consequently, akin to other challenging engineering optimization instances, significant fluctuations could be observed in the final results of steel frame optimization problems over multiple runs even using contemporary metaheuristics. Accordingly, the main focus of this study is to improve the solution quality as well as the stability of results in metaheuristic optimization of real-size steel frames using a recently developed framework so-called monitored convergence curve (MCC). Two enhanced variants of the well-known big bang-big crunch algorithm are adopted as typical contemporary metaheuristic algorithms to evaluate the usefulness of the MCC framework in steel frame optimization problems. The numerical experiments using challenging test examples of real-size steel frames confirm the efficiency of the MCC integrated metaheuristics versus their standard counterparts.

不可否认的是，在实践中，使用元启发法进行实际尺寸钢框架的优化设计存在主要挑战。通常，钢框架优化问题通常需要花费大量的处理时间，其中计算工作的主要部分专用于优化迭代期间进行的无数结构响应计算。此外，钢框架优化问题的内在复杂性甚至会导致甚至现代的或先进的元启发法的不良性能。除了此类问题的挑战性性质之外，可用轮廓列表中两个相邻钢截面的几何特性上的显着差异还会误导优化算法，并可能导致算法陷入较差的局部最优状态。所以，类似于其他具有挑战性的工程优化实例，即使使用当代的元启发式方法，在多次运行中钢框架优化问题的最终结果中也会观察到明显的波动。因此，本研究的主要重点是使用最近开发的框架（称为监控收敛曲线）来改善实尺寸钢框架的元启发式优化中的求解质量以及结果的稳定性。采用著名的大爆炸算法的两个增强变体作为典型的当代元启发式算法，以评估MCC框架在钢框架优化问题中的实用性。