School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
School of Mechatronic Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Sensors (Basel). 2018 Jul 11;18(7):2240. doi: 10.3390/s18072240.
Inspired by sensor coverage density and matching & preserving strategy, this paper proposes an Improved Artificial Bee Colony (IABC) algorithm which is designed to optimize bridge sensor placement. We use dynamic random coverage coding method to initialize colony to ensure the diversity and effectiveness. In addition, we randomly select the factors with lower trust value to search and evolve after food source being matched in order that the relatively high trust point factor is retained in the exploitation of food sources, which reduces the blindness of searching and improves the efficiency of convergence and the accuracy of the algorithm. According to the analysis of the modal data of the Ha-Qi long span railway bridge, the results show that IABC algorithm has faster convergence rate and better global search ability when solving the optimal placement problem of bridge sensor. The final analysis results also indicate that the IABC's solution accuracy is 76.45% higher than that of the ABC algorithm, and the solution stability is improved by 86.23%. The final sensor placement mostly covers the sensitive monitoring points of the bridge structure and, in this way, the IABC algorithm is suitable for solving the optimal placement problem of large bridge and other structures.
受传感器覆盖密度和匹配与保留策略的启发，本文提出了一种改进的人工蜂群（IABC）算法，旨在优化桥梁传感器的布置。我们使用动态随机覆盖编码方法初始化群体，以确保多样性和有效性。此外，我们在匹配食物源后随机选择信任值较低的因素进行搜索和进化，以便在利用食物源时保留相对较高的信任点因素，从而减少搜索的盲目性，提高收敛效率和算法的准确性。根据哈齐高速铁路大桥的模态数据的分析，结果表明，IABC 算法在解决桥梁传感器的最优布置问题时具有更快的收敛速度和更好的全局搜索能力。最终的分析结果还表明，IABC 的解精度比 ABC 算法高 76.45%，解的稳定性提高了 86.23%。最终的传感器布置主要覆盖了桥梁结构的敏感监测点，因此，IABC 算法适用于解决大型桥梁和其他结构的最优布置问题。
