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One main shortcoming of metaheuristic search techniques in structural optimization is the large number of time-consuming structural analyses required for convergence to a reasonable solution. This study is an attempt to apply the so-called upper bound strategy (UBS) as a simple, yet an efficient strategy to reduce the total number of structural analyses through avoiding unnecessary analyses during the course of optimization. Although, the usefulness of the UBS is demonstrated in conjunction with a big bang-big crunch algorithm developed for optimum design of truss structures, it can be integrated with any other metaheuristic technique which works on the basis of (μ+Λ) evolutionary model. The numerical investigations over three benchmark truss optimization instances reveal that the UBS can reduce the total number of required structural analyses of the standard BB-BC algorithm to a great extent.

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Bat-inspired (BI) algorithm is a recent metaheuristic optimization technique that simulates echolocation behavior of bats in seeking a design space. Along the same line with almost all metaheuristics, this algorithm also entails a large number of time-consuming structural analyses in structural design optimization applications. This study is focused on improving computational efficiency of the BI algorithm in optimum structural design. The number of structural analyses required by BI algorithm in the course of design optimization is reduced considerably by incorporating an upper bound strategy (UBS) into the solution procedure. The performance of the resulting algorithm, i.e. UBS integrated BI algorithm (UBI), is evaluated in discrete sizing optimization of large-scale steel skeletal structures designed for minimum weight according to American Institute of Steel Construction-Allowable Stress Design provisions. The numerical results verify that the UBI results in a significant gain in the computational efficiency of the standard algorithm.