Particle Swarm Optimization Algorithm Based on Greedy Strategy for Solving MOP Crowd Pricing Problem
Zehua Yue · 2018
Aiming at the problem of the task location and the crowdsourcing pricing of the task package under the bilateral mechanism, a multi-objective optimization (MOP) model was established, and the particle swarm optimization algorithm based on the task point perspective and the user perspective based greedy strategy was adopted as the fitness function (PSO) to solve. The analysis of the pricing law of the task and the judgment of the reasons for the unfinished task are considered. Firstly, we visualize the task and member's feature information, conduct preliminary analysis, and then further analyze the pricing law through K-means clustering. Secondly, we construct the Logit two-dimensional selection model, and through the function form and regression effect of the model, the impact task is not obtained. The reason for the completion is mainly caused by factors such as insufficient credit and time at the time of pricing.