Dynamic Economic Scheduling Problem Based on PSO-BBO Hybrid Optimization Algorithm

In order to solve the challenge of dynamic economic scheduling problem in financial economy, in view of the shortcomings of the existing machine Xi algorithms, this study introduces an innovative dynamic economic scheduling problem method based on PSO-BBO hybrid optimization algorithm. The new approach leverages the principles of biogeographic distribution theory to accurately identify and locate key influencing factors, and accordingly enables a sensible classification of indicators to reduce potential interference. At the same time, using the unique mechanism of PSO-BBO hybrid optimization algorithm, the design strategy of economic scheduling is cleverly constructed. The empirical results show that the proposed scheme shows a significant improvement compared with the traditional machine Xi algorithm in terms of the accuracy of the dynamic economic scheduling problem and the processing efficiency of key factors, showing its obvious strong advantages. In intelligent finance, the dynamic economic scheduling problem plays a crucial role, which can accurately predict and optimize the growth trend and output results of the dynamic economic scheduling problem of the financial economy. However, in the face of complex simulation tasks, traditional machine Xi algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this problem, this study introduces a new idea of dynamic economic scheduling problem optimized by PSO-BBO hybrid optimization algorithm, and accurately controls the influencing parameters through the bibliographic distribution theory, and uses it as the road map for index allocation, and then uses the PSO-BBO hybrid optimization algorithm to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the dynamic economic scheduling problem of financial economy, the simulation scheme based on PSO-BBO hybrid optimization algorithm successfully overcomes the shortcomings of traditional machine learning Xi algorithm, and significantly improves the accuracy and operation efficiency of simulation.