计算机应用研究2024，Vol.41Issue(1)：159-164,169,7.DOI:10.19734/j.issn.1001-3695.2023.04.0186
面向异构效用的移动群智感知多目标任务分配
Multi-objective task assignment towards heterogeneous utilities in mobile crowdsensing
摘要
Abstract
Most of the existing MCS task allocation methods often only consider the unilateral utility of workers or platforms,and the composition of utility is not comprehensive enough.Therefore,this paper designed a heterogeneous utility mechanism for both workers and platforms based on the worker reputation index and task proficiency index.It proposed a dual-population competitive multi-objective evolutionary algorithm(DCMEA)to obtain the optimal worker and platform heterogeneous utilities.The algorithm firstly initialized the population using a stochastic greedy algorithm,then divided the population into a winner population and a loser population using a binary bidding tournament algorithm,and employed different evolutionary strategies for each population.Finally,this paper proposed the repair operator to make the invalid individuals in the evolution process satisfy the constraint.Experiments on real-world datasets show that DCMEA converges faster than the baseline algorithm,and can find more accurate and stable task allocation solution sets,while maintaining its performance in more complex scenarios.关键词
移动群智感知/多任务分配/多 目标优化/双种群竞争进化/信誉指数/任务熟练指数Key words
mobile crowdsensing/multi-task allocation/multi-objective optimization/competitive evolution of two popula-tions/reputation index/task proficiency index分类
信息技术与安全科学引用本文复制引用
傅彦铭,陆盛林,祁康恒,许励强,陈嘉元..面向异构效用的移动群智感知多目标任务分配[J].计算机应用研究,2024,41(1):159-164,169,7.基金项目
国家自然科学基金资助项目(61962005) （61962005）
