PSO Algorithm for Intelligent Big Data Financial Management

Intelligent big data financial management is a new industry that uses intelligent technology and big data analysis to process and manage financial data. As a key problem in this industry, how to find the optimal solution has attracted more and more attention. Particle swarm optimization (PSO) and genetic algorithm (GA) are mainstream optimization algorithms used in solving the optimal solution. In this paper, we evaluated and compared the optimal solution finding fitness value, stability, and time performance of PSO algorithm and GA algorithm in intelligent big data financial management. We found that the optimal fitness value of PSO algorithm is 2000. It performs much better than the fitness value found by GA algorithm. First, this algorithm can obtain more accurate and reliable financial data than GA algorithm. In addition, the algorithm is more stable and can find the optimal solution in a shorter time.