Distributed Digital Economy Scheduling Problem Based on Improved MNPSO Algorithm

Abstract

With the rapid development of cloud computing, Big data mining and other fields, people pay more and more attention to the distributed economic scheduling problem. To address this issue, this article proposes a solution based on the improved Manganese Particle Swarm Optimization (MNPSO) algorithm for solving array location services and distributed digital product scheduling solutions. This scheme categorizes the products purchased by consumers to obtain the optimal solution, and assigns it to different users in different regions to solve practical problems. Next, this article utilizes the improved MNPSO algorithm to calculate the expected utility function and constraint value, thereby establishing a distributed model for specific target customers. Afterwards, this article compares the performance of the algorithm of this model with other algorithms, and the comparison results show that the efficiency of the distributed digital economy scheduling model based on the improved MNPSO algorithm is relatively high. Compared to other algorithms, the efficiency of this algorithm can reach 84%.