The performance-based optimum seismic design of steel frames is one of the most complicated and computationally demanding structural optimization problems. Metaheuristic optimization methods have been successfully used for solving engineering design problems over the last three decades. A very recently developed metaheuristic method called school-based optimization (SBO) will be utilized in the performance-based optimum seismic design of steel frames for the first time in this study. The SBO actually is an improved/enhanced version of teaching–learning-based optimization (TLBO), which mimics the teaching and learning process in a class where learners interact with the teacher and between themselves. Ad hoc strategies are adopted in order to minimize the computational cost of SBO results. The objective of the optimization problem is to minimize the weight of steel frames under interstory drift and strength constraints. Three steel frames previously designed by different metaheuristic methods including particle swarm optimization, improved quantum particle swarm optimization, firefly and modified firefly algorithms, teaching–learning-based optimization, and JAYA algorithm are used as benchmark optimization examples to verify the efficiency and robustness of the present SBO algorithm. Optimization results are compared with those of other state-of-the-art metaheuristic algorithms in terms of minimum structural weight, convergence speed, and several statistical parameters. Remarkably, in all test problems, SBO finds lighter designs with less computational effort than the TLBO and other methods available in metaheuristic optimization literature.

中文翻译：

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基于性能优化的钢框架抗震设计校本优化

基于性能的钢框架优化抗震设计是最复杂和计算量最大的结构优化问题之一。在过去的三年中，元启发式优化方法已成功用于解决工程设计问题。在本研究中，最近开发的一种称为基于学校的优化 (SBO) 的元启发式方法将首次用于基于性能的钢框架优化抗震设计。SBO 实际上是基于教学的优化 (TLBO) 的改进/增强版本，它模仿课堂中的教学和学习过程，学习者与教师以及他们之间进行互动。采用特别策略以最小化 SBO 结果的计算成本。优化问题的目标是在层间漂移和强度约束下最小化钢框架的重量。以粒子群优化、改进的量子粒子群优化、萤火虫和改进的萤火虫算法、基于教学的优化和 JAYA 算法等不同元启发式方法设计的三个钢框架作为基准优化示例，验证了算法的效率和鲁棒性。目前的 SBO 算法。优化结果与其他最先进的元启发式算法在最小结构权重、收敛速度和几个统计参数方面进行了比较。值得注意的是，在所有测试问题中，