TentISSA-BPNN: a novel evaluation model for cloud service providers for petroleum enterprises

To investigate how the petroleum industry evaluates and selects powerful cloud service providers, first, an evaluation index system including 25 indices such as scalability and private data protection is built. This index system can systematically examine the comprehensive strengths of cloud service providers. Aiming to solve the problems that the traditional expert evaluation method has high requirements on expert experience and is easily affected by subjective factors, a novel artificial intelligence evaluation model named TentISSA-BPNN is proposed. The objective evaluation ability of this model can be effectively used in evaluation research on cloud service providers for petroleum enterprises. In this model, the SSA algorithm is optimized by Tent chaotic mapping and adaptive inertia weight; an algorithm, TentISSA, that has good stability and fast convergence speed is designed and proposed; and the BPNN is improved with TentISSA to obtain more accurate evaluation results. To evaluate the performance of the TentISSA algorithm, nine unimodal and multimodal functions are selected in this paper to test the convergence accuracy. Then, seven models are selected as the control groups to validate the effectiveness and performance of the TentISSA-BPNN evaluation model proposed in this paper. Finally, the preprocessed data of the candidate cloud service providers are input into the trained neural network model proposed in this paper for evaluation. Based on the ranking of the evaluation scores, the comprehensive strengths of the cloud service providers are obtained to provide a decision-making reference for managers of petroleum enterprises in the process of choosing cloud service providers.