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Q-Learning-Based Hyperheuristic Evolutionary Algorithm for Dynamic Task Allocation of Crowdsensing
IEEE Transactions on Cybernetics ( IF 11.8 ) Pub Date : 2021-10-04 , DOI: 10.1109/tcyb.2021.3112675
Jian-Jiao Ji , Yi-Nan Guo , Xiao-Zhi Gao , Dun-Wei Gong , Ya-Peng Wang

Task allocation is a crucial issue of mobile crowdsensing. The existing crowdsensing systems normally select the optimal participants giving no consideration to the sudden departure of mobile users, which significantly affects the sensing quality of tasks with a long sensing period. Furthermore, the ability of a mobile user to collect high-precision data is commonly treated as the same for different types of tasks, causing the unqualified data for some tasks provided by a competitive user. To address the issue, a dynamic task allocation model of crowdsensing is constructed by considering mobile user availability and tasks changing over time. Moreover, a novel indicator for comprehensively evaluating the sensing ability of mobile users collecting high-quality data for different types of tasks at the target area is proposed. A new $Q$ -learning-based hyperheuristic evolutionary algorithm is suggested to deal with the problem in a self-learning way. Specifically, a memory-based initialization strategy is developed to seed a promising population by reusing participants who are capable of completing a particular task with high quality in the historical optima. In addition, taking both sensing ability and cost of a mobile user into account, a novel comprehensive strength-based neighborhood search is introduced as a low-level heuristic (LLH) to select a substitute for a costly participant. Finally, based on a new definition of the state, a $Q$ -learning-based high-level strategy is designed to find a suitable LLH for each state. Empirical results of 30 static and 20 dynamic experiments expose that this hyperheuristic achieves superior performance compared to other state-of-the-art algorithms.

中文翻译:

基于Q学习的群体感知动态任务分配超启发式进化算法

任务分配是移动群智感知的一个关键问题。现有的群体感知系统通常会选择最佳参与者,而没有考虑移动用户的突然离开,这会严重影响感知周期较长的任务的感知质量。此外,移动用户采集高精度数据的能力对于不同类型的任务通常被视为相同,导致竞争用户提供的某些任务的数据不合格。为了解决这个问题,通过考虑移动用户可用性和任务随时间变化构建了一个动态的群智感知任务分配模型。此外,还提出了一种新的指标,用于综合评估移动用户在目标区域为不同类型的任务收集高质量数据的感知能力。一个新的 $Q$ 提出了基于学习的超启发式进化算法以自学习的方式处理该问题。具体来说,开发了一种基于记忆的初始化策略,通过重用能够在历史最优中高质量完成特定任务的参与者来播种有前途的种群。此外,考虑到移动用户的感知能力和成本,引入了一种新的基于综合实力的邻域搜索作为低级启发式(LLH)来选择昂贵参与者的替代品。最后,基于状态的新定义, $Q$ -learning-based high-level strategy 旨在为每个状态找到合适的 LLH。30 个静态和 20 个动态实验的实证结果表明,与其他最先进的算法相比,这种超启发式算法具有卓越的性能。
更新日期:2021-10-04
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