融合社区检测的协作众包任务分配方法  

Collaborative crowdsourcing task allocation method fusing community detection

作　　者：胡林波 倪志伟[1,2] 程家乐 刘文涛 朱旭辉[1,3] HU Linbo;NI Zhiwei;CHENG Jiale;LIU Wentao;ZHU Xuhui(School of Management,Hefei University of Technology,Hefei Anhui 230009,China;Key Laboratory of Process Optimization and Intelligent Decision-making,Ministry of Education(Hefei University of Technology),Hefei Anhui 230009,China;Intelligent Interconnected System Anhui Provincial Laboratory(Hefei University of Technology),Hefei Anhui 230009,China)

机构地区：[1]合肥工业大学管理学院,合肥230009 [2]过程优化与智能决策教育部重点实验室(合肥工业大学),合肥230009 [3]智能互联系统安徽省实验室(合肥工业大学),合肥230009

出　　处：《计算机应用》2025年第2期534-545,共12页journal of Computer Applications

基　　金：国家自然科学基金资助项目(72171073,72371088);安徽省科技重大专项(201903a05020020)。

摘　　要：针对传统协作众包任务分配中忽视工人协作关联的问题,将工人之间的社交及历史合作关系纳入考虑范畴,提出一种融合社区检测的协作众包任务分配方法。首先,利用社区检测算法挖掘众包工人之间潜在的社交关系,形成候选社群;其次,定义协作度、交互成本和众包任务分配效用等要素后,构建综合考虑技能覆盖率、信誉度及预算成本的协作众包任务分配模型;再次,引入Piece-Wise混沌映射、柯西分布逆累积函数算子、自适应正切飞行算子和麻雀警戒机制等策略,并提出改进沙猫群优化(SCSO)算法——TSCSO;最后,利用TSCSO算法对前述模型进行求解。在不同规模真实数据集合成的算例上的实验结果表明,所提算法可使任务分配成功率维持在90%及以上水平,相较于其他改进智能算法任务分配效用平均提升20.08%~53.38%,验证了所提算法在协作众包任务分配问题中的适用性、稳定性和有效性。To address the issue of neglecting workers' collaborative relationships in traditional collaborative crowdsourcing task allocation,a collaborative crowdsourcing task allocation method fusing community detection was proposed,by considering the social and historical cooperative relationships among workers.Firstly,potential social relationships among crowdsourced workers were mined by a community detection algorithm to establish candidate communities.Secondly,after defining factors such as degree of collaboration,interaction cost,and utility of task allocation,a model for collaborative crowdsourcing task allocation was developed by considering skill coverage,credibility,and budget comprehensively.Thirdly,the strategies such as Piece-Wise chaotic mapping,inverse cumulative function operator based on Cauchy distribution,adaptive tangent flight operator,and sparrow warning mechanism were introduced and an optimized Sand Cat Swarm Optimization(SCSO) algorithm — TSCSO was proposed.Finally,TSCSO algorithm was used to solve the aforementioned model.Experimental results on examples synthesized from real datasets of different scales demonstrate that the proposed algorithm has the task allocation success rate of at least 90%.Furthermore,TSCSO algorithm improves the average task allocation utility ranging by 20.08% to 53.38% compared to other optimized intelligent algorithms,verifying the proposed algorithm's applicability,stability,and efficacy in collaborative crowdsourcing task allocation problems.

关 键 词：协作众包 社区检测 协作候选社群 任务分配 沙猫群优化算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]