Enhancing business analytics predictions with hybrid metaheuristic models: a multi-attribute optimization approach

Abstract

This approach aims to optimize business analytical predictions through multiattribute optimization using a hybrid metaheuristic model based on the modified particle swarm optimization (MPSO) and gravitational search optimization (GSO) algorithms. This research uses a variety of data, such as revenue, expenses, and customer behavior, to improve predictive modeling and achieve superior results. MPSO, an interparticle collaborative mechanism, efficiently explores the search space, whereas GSO models’ gravitational interactions between particles to solve optimization problems. The integration of these two algorithms can improve the performance of business analytical predictions by increasing model precision and accuracy, as well as speeding up the optimization process. Model validation test results, precision 95.60%, recall 96.35%, accuracy 96.69%, and F1 score 96.11%. This research contributes to the development of more sophisticated and effective business analysis techniques to face the challenges of an increasingly complex business world.