Abstract:

Predicting currency exchange rates, especially volatile pairs like GBP/USD, is challenging because prices depend on many interacting economic, political, and market factors. Traditional forecasting approaches often struggle with the nonlinear, non-stationary behavior of financial time series. The paper proposes a Multi-Layer Perceptron (MLP) whose hyperparameters are optimized with Particle Swarm Optimization (PSO). PSO automatically searches the hyperparameter space, replacing slow manual tuning and finding better network configurations. Experiments show the PSO-optimized MLP reduces RMSE by 45.33\% relative to a manually tuned baseline, indicating markedly improved predictive accuracy under market volatility. The study demonstrates that swarm-intelligence optimization is an effective, repeatable way to build stronger neural forecasts for foreign exchange. By improving forecasting reliability, the work supports SDG 8 and SDG 9 through smarter, AI-driven financial decision support. Swarm intelligence proves practical for robust forex forecasting. Such models can assist traders, firms, and regulators in risk management and efficient currency operations.