移动群智感知网络能够有效利用社会资源以提供快速便捷的服务模式，在诸如防暴反恐、污染监测、智慧城市中将广泛应用。其中，高效的多用户感知任务分发已成为该领域重要研究方向。任务分发时，系统期望以最小的资源消耗获得较高的感知效率，但较少的激励将直接降低用户参与感知的满意度。可见，感知效率与用户满意度之间的矛盾严重制约着群智感知网络的快速发展。因此，如何同时优化感知效率和用户满意度已成为该研究方向的关键问题。为此，本课题拟挖掘用户多元化社会网络特性，以感知任务再分配、资源匹配和激励共享的合作模式为切入点，建立一种多元空间协同感知模型，揭示系统感知效率和用户满意度的耦合机理；为最大化系统感知效率和用户满意度，建立移动群智感知网络任务分发的自适应动态多目标优化模型，提出一种超启发式进化多目标优化算法。本课题将为移动群智感知网络资源最优配置提供理论基础和关键技术支撑，具有重要的研究意义和广阔的应用前景。

Task allocation is a core problem need to be solved in mobile crowdsensing systems, and is the basis and precondition of other key technologies. This project focus on the multi-space collaboration sensing model through Personal Profile Analysis, and the task reallocation、resource matching and incentive sharing. On this basis, propose adaptive dynamic multi-objective optimization model and the corresponding algorithm when considering task allocation. The main research content of this project includes two aspects: on the one hand, through analysis of the social network behavior of the participants, a multivariate space collaborative sensing mechanism is designed to improve the sensing efficiency of the crowdsensing system so as to maximize resource utilization. On the other hand, analysis the drawbacks of the existing static unilateral performance when designing task schedule strategies, a dynamic multi-objective task allocation optimization model which the sensing efficiency and participant satisfactory will be maximized simultaneously is proposed, and a corresponding hyper-heuristic based evolutionary learning algorithm is designed to obtain a balanced task scheduling strategy on the above two system performances. This project will promote the development and the practical application of mobile crowdsensing technology, and has a great important meaning in the science and engineering application.