Optimal Allocation of Enterprise Resources Based on the Intelligent Algorithm from the Perspective of Multidimensional Dynamic Innovation

Abstract

Faced with the upsurge of digital technology, many small and medium-sized startups are aware of the opportunities and challenges it brings and are actively responding to digital transformation. Digital transformation is a type of strategic change with certain risks. On the one hand, this requires business leaders to have a sense of change, and on the other hand, it also requires certain resources to be invested. As a result, in the real world, not all small and medium-sized entrepreneurial firms have the ability or inclination to implement digital transformation. SMEs’ digital transformation has become a key factor in promoting the national economy’s transition from new and old kinetic energy and providing new prospects for development of small and medium firms. As a starting point, this study devises an index system for assessing the digital maturity of small and medium-sized businesses. Based on the AHP method in the multicriteria framework, a hierarchical structure is established for each index, a three-layer basic framework is established, and the weight of the evaluation index is calculated. Second, this work establishes a BP network model for digital transformation maturity assessment of startups based on AHP and establishes a three-layer BP network structure. This work aims to optimize BP network using the improved ISFLA algorithm. It improves the adaptive step size update formula in the SFLA algorithm by using the mutation operator and improves the evolution method of the worst individual of the frog to the simultaneous evolution of multiple poor individuals. The constructed ISFLA-BP algorithm is then used to evaluate the digital transformation maturity of small and medium-sized startups. Finally, systematic and comprehensive experiments are carried out to verify superiority as well as feasibility of the method.