基于Worker权重差分进化与Top-k排序的结果汇聚算法

Result aggregation algorithm based on differential evolution and Top-k ranking in learning Worker’s weight

针对众包结果汇聚中最优排序结果选取的时效性问题,提出了Worker权重的高效快速汇聚算法。其中Worker权重的差分进化算法重点考虑众包Worker完成排序任务存在的差异性问题,基于目标函数和约束条件中Worker完成任务的不确定性和差异性影响,建立基于差分进化算法的Worker权重优化模型,获取多数据项场景下候选结果最优权重,实现Worker权重与任务对结果性能需求匹配的最大化;提出基于Top-k排序的优化模型求解算法,针对多数据项场景下候选结果的Top-k排序选取,在合适的k值下可快速求解上述模型,获得各Worker的优化权重。所提出的基于优化的Worker权重可实现结果汇聚的匹配性与匹配速度优化,即在提升结果汇聚速度的同时,具有优化的汇聚结果性能。定性分析证明了算法的正确性,仿真实验结果也验证了算法的效果,与相关算法对比,所提算法的综合性能最优。

To solve the problem of quickly obtaining the optimal ranking result in the crowdsourcing result aggregation,an efficient and effective aggregation algorithm of Worker’s weight was proposed.The Worker’s weight optimization model based on differential evolution algorithm focused on the uncertainties and differences of Workers completing ranking tasks,the uncertainties and differences were reflected in the objective function and constraint conditions of the model.This model obtained the optimal weight of candidate results,and maximized the matching between Worker’s weight and result performance.Then,the optimization model solving method based on Top-k ranking was proposed to quickly obtain the optimal Worker’s weight with the appropriate k value for specific multi-data items ranking scenario.The optimization of Worker’s weight could realize optimized performance and speed of the result aggregation.The correctness of the algorithm is verified by qualitative analysis,the effectiveness and efficiency of the algorithm is verified by the simulation results,and the comparison with the relevant algorithms shows the optimal comprehensive performance of the algorithm.