基于GACO的群智感知参与者选择方法研究

参与者选择方法作为群智感知研究的重要内容之一，现有研究还存在不足，只单一考虑任务发布时间或任务区域覆盖等属性，导致选择的参与者执行任务效率较差。因此针对这一问题综合考虑任务时间和任务区域覆盖等约束条件下，为实现任务执行效率最高和群智感知平台激励成本最少的优化目标，提出一种基于贪婪蚁群算法的群智感知参与者选择方法（PS-GACO）。该方法主要通过候选参与者聚集蚂蚁信息素浓度的多少准确选出适合执行发布任务的参与者，大大提高了任务执行效率。最后通过仿真实验将提出的PS-GACO方法与普通参与者选择方法进行比较，实验结果表明PS-GACO在算法运行时间、任务执行效率以及激励成本等方面都优于其他两种方法，对于群智感知参与者选择有很好的应用前景。

Research on selection method of crowd sensing participants based on GACO

Participant selection method is one of the important contents of crowd sensing research. Existing research still has some shortcomings. Only the attributes such as task time or task area coverage are considered, which makes the selected participants perform tasks less efficiently. Therefore, in order to comprehensively consider the task time and task area coverage constraints, this paper proposed a selection method of crowd sensing participant based on the greedy ant colony algorithm(PS-ACO) to achieve the highest task execution efficiency and the minimum incentive cost of the crowd sensing platform. The method mainly selected the participants who were suitable for performing the publishing task by the concentration of the ant pheromone concentration of the candidate participants, and greatly improved the task execution efficiency. Finally, it compared the proposed PS-GACO method with the common participant selection method through simulation experiments. The experimental results show that PS-GACO is superior to the other two methods in terms of algorithm running time, task execution efficiency and incentive cost, and has a good application prospect for the crowd sensing participant selection.