Title: Adaptability analysis of artificial intelligence and evolutionary computation in modelling and prediction of complex economic systems

Authors: Na Tao

Addresses: Shenyang University, Shenyang, Liaoning Province, China

Abstract: The rapid advancements in Artificial Intelligence (AI) and Evolutionary Computation (EC) have paved the way for innovative solutions to complex economic modelling and prediction challenges. In this paper, we present a novel approach that integrates Deep Belief Networks (DBNs) with Particle Swarm Optimisation (PSO) to enhance the accuracy and robustness of exchange rate predictions in the Forex market. The proposed hybrid DBN-PSO model leverages the deep learning capabilities of DBNs to capture intricate data patterns, while PSO optimises the hyperparameters to achieve optimal performance. Extensive experiments on historical Forex data demonstrate that the DBN-PSO model significantly outperforms compared models in terms of four metrics. Visual analyses further illustrate the close alignment between predicted and actual exchange rates, underscoring the model's predictive accuracy and reliability. This research contributes to the advancement of economic forecasting by providing a robust and efficient tool for modelling and predicting complex economic systems.

Keywords: artificial intelligence; evolutionary computation; complex economic systems; adaptability analysis.

DOI: 10.1504/IJCAT.2025.150331

International Journal of Computer Applications in Technology, 2025 Vol.77 No.3/4, pp.247 - 254

Received: 11 Sep 2024
Accepted: 24 Jun 2025
Published online: 09 Dec 2025 *

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