Prediction Using Particle Swarm Optimization Combined with Grey System Theory

This document presents a MATLAB implementation for prediction using a hybrid approach that combines Particle Swarm Optimization (PSO) with Grey System Theory. The code demonstrates high accuracy, making it widely applicable for various forecasting scenarios. Particle Swarm Optimization is a population-based optimization algorithm that simulates collective behavior to find optimal solutions through position and velocity updates using social and cognitive components. Grey System Theory provides a methodology for handling systems with uncertain information through grey relational analysis and GM(1,1) prediction models. The integration of these two methods enables more accurate forecasting of future trends and outcomes by optimizing grey model parameters using PSO's global search capabilities. Furthermore, the code can be enhanced through modifications such as adaptive inertia weights in PSO, improved boundary handling mechanisms, and optimized grey model initialization procedures to further increase prediction accuracy and reliability. This hybrid approach proves particularly valuable for prediction problems involving complex systems, offering better understanding and mastery of system dynamics and development patterns through MATLAB's computational framework.