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

Abstract

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.

Keywords

• crowdsourcing;result aggregation;differential evolution algorithm;learning to rank