Wright–Fisher multi-strategy trust evolution model with white noise for Internetware

A trust evolution model plays an important role in ensuring and predicting the behaviors of entities in Internetware system. Most of the current trust evolution models almost adopt expertise or average weight method to calculate entities’ trust incomes, and focus on two strategies (‘full trust’, ‘full distrust’) to analyze trust behaviors. In addition, the researches on dynamics evolution models fail to consider the factor of noise, and cannot effectively prevent free-riding phenomenon. In this paper, a trust measurement based on Quality of Service (QoS) and fuzzy theory by considering timeliness of history data is proposed to improve the accuracy of trust measurement results. Furthermore, a trust evolution model based on Wright–Fisher and the evolutionary game theory is proposed. This model considers multi-strategy and noise problems to improve the accuracy of prediction and adaptability of model in complex networks. Meanwhile, in order to solve the free-riding problem, and improve the trust degree of a system, an incentive mechanism is established based on evolutionary game theory to inspire entities to select trust strategies. The simulation results show that this model has good adaptability and accuracy. In addition, this model can effectively improve network efficiency, and make trust income reach an optimal value, so as to improve trust degree of a system.