基于用户满意效用的空间众包任务分配方法

针对生活中专车类空间众包用户存在偏好和延时等待的实际情况，提出一种基于用户满意效用的空间众包任务分配方法IGSO-SSCTA。首先，定义了由用户偏好效用、延时等待效用和任务完成期望组成的用户满意效用；其次，构建了基于用户满意效用的空间众包任务分配（SSCTA）模型；接着，通过离散编码、反向学习协同初始化、四种改进移动策略、自适应选择和不可行解处理，提出一种适用该模型的改进离散萤火虫群优化（IGSO）算法；最后，利用IGSO算法对前述模型进行求解。不同规模数据集上的实验结果表明，所提方法和考虑时间最小化分配、考虑路程最小化分配、随机分配三种策略相比，用户满意效用分别提高了提升了9.64%、11.77%、15.70%；所提算法与贪婪算法和其他改进萤火虫算法相比，也有更好的稳定性和收敛性。

Task allocation method of spatial crowdsourcing based on user satisfaction utility

In view of the actual situations such as the preference and the delay waiting of spatial crowdsourcing users of ride-hailing in life， a task allocation method of spatial crowdsourcing based on user satisfaction utility called IGSO（Improved discrete Glowworm Swarm Optimization）-SSCTA（Spatial Crowdsourcing Task Allocation based on user Satisfaction utility） was proposed. Firstly， user satisfaction utility was defined， which was composed of user preference utility， delay waiting utility and task completion expectation. Secondly， SSCTA model was constructed based on user satisfaction utility. Thirdly， IGSO algorithm was proposed by discrete coding， the initialization of reverse learning collaboration， four improved mobile strategies， adaptive selection strategy and treatment of infeasible solutions. Finally， IGSO algorithm was used to solve the above model. Experimental results on different scale datasets show that compared with the three allocation strategies of time minimization， distance minimization and random allocation， the user satisfaction utility of the proposed method is improved by 9.64%， 11.77% and 15.70% respectively， and the proposed algorithm has better stability and convergence than the greedy algorithm and other improved glowworm algorithms.