任务和参与者匹配意愿规则约束下的移动群智感知多任务分配  

Multi-task allocation under task and participant matching willingness rules constraints in mobile crowdsensing

作　　者：杨凯程 张昕[1] 胡佳豪 周超然[1] 李泽睿 Yang Kaicheng;Zhang Xin;Hu Jiahao;Zhou Chaoran;Li Zerui(College of Computer Science&Technology,Changchun University of Science&Technology,Changchun 130022,China)

机构地区：[1]长春理工大学计算机科学技术学院,长春130022

出　　处：《计算机应用研究》2025年第4期1050-1057,共8页Application Research of Computers

基　　金：国家重点研发计划资助项目(2023YFB3308900);吉林省自然科学基金资助项目(20200201182JC,YDZJ202301ZYTS422);吉林省教育厅科学研究项目(JJKH20250519KJ)。

摘　　要：当前,大部分移动群智感知(MCS)多任务分配方法仅考虑时间约束,因忽略任务、参与者双方匹配意愿,难以保证真实场景下的任务接受率,无法最大化平台利润。为解决上述问题,提出了一种引入任务、参与者双方偏好的MCS任务分配算法,称为免疫遗传鲸优算法(IGWOA)。该算法基于鲸鱼优化算法的智能搜索机制解决MCS任务分配的资源配置问题,并在搜索、包围猎物阶段分别引入免疫遗传方法中的交叉、免疫思想,增加任务分配策略的多样性、提高解集的质量,在气泡网捕食阶段结合变异思想提升算法的局部搜索能力,实现了快速收敛和最优任务分配策略生成。实验结果表明,IGWOA在不同数据规模下的平台利润、任务分配率和参与者平均分配任务数量指标上优于基线方法,且在算法时间复杂度方面表现良好,证明其可有效解决时间和匹配意愿规则双重约束的MCS多任务分配问题。Currently,most mobile crowdsensing(MCS)multi-task allocation methods only consider time constraints,neglecting the matching willingness of tasks and participants.This makes it hard to guarantee the task acceptance rate in real scenarios and maximize the platform’s profit.To solve this,this paper proposed an MCS task assignment algorithm introducing task and participant preferences,called immune genetic whale optimization algorithm(IGWOA).The algorithm used the intelligent search mechanism of whale optimization algorithms to solve the resource allocation problem in MCS task allocation.It introduced cross and immunity ideas from immune genetic methods in the search and encircling prey stages to increase strategy diversity and solution quality.In the bubble net predation stage,it combined the mutation idea to enhance local search ability.It achieved fast convergence and generated the optimal assignment strategy.The experimental results show that IGWOA outperforms the baseline method in terms of platform profits,task assignment rates,and average task assignment numbers for participants under various data scales.And it also has good algorithmic time complexity,which proves that it can effectively solve the MCS multi-task allocation problem with dual constraints of time and matching willingness rules.

关 键 词：移动群智感知 多任务分配 匹配意愿 鲸鱼优化算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]