基于改进粒子群算法的空间众包任务分配模型

Spatial crowdsourcing task allocation model based on improved particle swarm optimization

如何进行高效合理的任务分配是当前空间众包(SC)研究中的关键问题之一。针对SC分配效能低的问题,建立了最佳质量任务分配模型(maximum quality task assignment model,MQTAM),并提出了基于改进粒子群算法的空间众包任务分配算法(SCTAM_PSO)。该模型充分考虑了工作者到达工作地点后完成任务的时间延迟、完成任务的可信度等因素,通过SCTAM_PSO算法智能搜索最佳分配方案以最大化提高任务完成质量。实验结果及分析表明,MQTAM和SCTAM_PSO具有一定的有效性与可行性。

How to allocate tasks efficiently and reasonably is one of the key problems in the research of spatial crowdsourcing.This paper proposed a MQTAM to solve the problem of low allocation efficiency of SC,and proposed an improved particle swarm optimization algorithm. The model took into account the time delay of task completion and the reliability of task completion,and used SCTAM_PSO algorithm to search the optimal assignment scheme to improve the task completion quality. The experimental results and analysis show that the MQTAM and SCTAM are effective and feasible.