The conformable fractional accumulation generating operator (CF-AGO) can effectively handle information differences and deeply

explore the laws of information development. Nevertheless, the CF-AGO fails to satisfy the highly crucial new information priority

(NIP) principle. In this paper, a novel conformable fractional accumulation generating operator (NCF-AGO), which meets the NIP

principle under certain conditions, is introduced firstly. Then an improved conformable fractional grey multivariate model with

variable NCF-AGO is constructed. Both linear and nonlinear correction terms are considered in the model structure to fit data

sequences with different features. The quantum particle swarm optimization algorithm is adopted to obtain the optimal accumulation

orders and the optimal power exponent of the nonlinear correction term. In order to avoid the situation where overfitting of the model leads to poor prediction results, the Tikhonov regularization method, which includes the conventional least squares method as a special case, is proposed solve the involved model parameters. Finally, a case study from bending strength of concrete is given to show the effectiveness of the proposed model and its advantages over the well-known GM(1,N) model and several existing grey multivariate models.