The snow ablation optimizer (SAO) is a meta-heuristic technique used to seek the best solution for sophisticated problems. In response to the defects in the SAO algorithm, which has poor search efficiency and is prone to getting trapped in local optima, this article suggests a multi-strategy improved (MISAO) snow ablation optimizer. It is employed in the unmanned aerial vehicle (UAV) path planning issue. To begin with, the tent chaos and elite reverse learning initialization strategies are merged to extend the diversity of the population; secondly, a greedy selection method is deployed to retain superior alternative solutions for the upcoming iteration; then, the Harris hawk (HHO) strategy is introduced to enhance the exploitation capability, which prevents trapping in partial ideals; finally, the red-tailed hawk (RTH) is adopted to perform the global exploration, which, enhances global optimization capability. To comprehensively evaluate MISAO’s optimization capability, a battery of digital optimization investigations is executed using 23 test functions, and the results of the comparative analysis show that the suggested algorithm has high solving accuracy and convergence velocity. Finally, the effectiveness and feasibility of the optimization path of the MISAO algorithm are demonstrated in the UAV path planning project.

Data asset value assessment is of strategic significance to the development of data factorization, in order to solve the problems of strong assessment subjectivity and low assessment efficiency and accuracy in traditional assessment methods. This article introduces the SLPDBO-BP data asset assessment model for data asset value assessment. Firstly, the sinusoidal chaos mapping strategy, the Levy flight strategy and the fusion of adaptive weight variation operators are integrated to increase the population diversity of the algorithm, broaden the search range, and augment the global optimization capability of the algorithm. Secondly, in an attempt to comprehensively evaluate the optimization performance of SLPDBO, a series of numerical optimization experiments are carried out with 20 test functions and with popular optimization algorithms and dung beetle optimizer (DBO) algorithms with different improvement strategies. Finally, in order to verify the effectiveness of the proposed algorithm in data asset value assessment, the SLPDBO algorithm is combined with backpropagation (BP) to establish the SLPDBO-BP model for data asset value assessment, and the acquired data sets are used in the proposed model for data asset value assessment. The experimental results show that the SLPDBO-BP model performs well in assessment accuracy, and its assessment indexes mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) are reduced by 35.1%, 37.6% and 38.7%, respectively, compared with the dung beetle optimizer backpropagation (DBO-BP) model, and its evaluation efficiency is improved, and the proposed model demonstrates better evaluation simulation effects by remarkably outperforming other models in terms of evaluation accuracy and error level.