This study proposes an algorithm titled a statistical firefly algorithm (SFA) for truss topology optimization. In the proposed algorithm, historical results of fireflies' motions are used in hypothesis testing to limit the motions of fireflies that are suggested by current information exchanges between fireflies only to those that are potentially useful. Hypothesis testing is applied to the mechanism of an ordinary firefly algorithm (FA) without changing its structure. As a result, the implementation of the proposed algorithm is simple and straightforward. Limiting the motions of fireflies to those that are potential useful results in reduction of firefly evaluations, and, subsequently, reduction of computational efforts. To test the validity and efficiency of the proposed algorithm, it is used to solve several truss topology optimization problems, including some benchmark problems. It is found that the added statistical strategy in the SFA significantly enhances the performance of the original FA in terms of computational efforts while still maintains the quality of the obtained results.

翻译：本研究提出了一种名为统计萤火虫算法的算法，用于桁架拓扑优化。在所提出的算法中，萤火虫运动的历史结果被用于假设检验，以限制萤火虫的运动——这些运动仅由萤火虫之间当前的信息交换所建议——仅限于那些可能有用的情况。假设检验被应用于普通萤火虫算法的机制中，而未改变其结构。因此，所提算法的实现简单直接。将萤火虫的运动限制在可能有益的范围内，减少了萤火虫的评估次数，从而降低了计算开销。为了测试所提算法的有效性和效率，我们将其用于求解多个桁架拓扑优化问题，包括一些基准问题。结果发现，SFA中添加的统计策略在保持所得结果质量的同时，显著提升了原始FA在计算开销方面的性能。