## 1 Introduction
This diversity-driven approach helps explore various possibilities and avoids getting stuck in local optima, making it suitable for probabilistic prediction where uncertainty is prevalent. PSO imitates the social behavior of particles in a swarm. Particles move through the solution space, adjusting their positions based on their own experiences and the swarm's best results. This balance between local and global exploration allows PSO to effectively handle uncertainty and produce diverse solutions, making it fitting for probabilistic prediction tasks where multiple outcomes need to be considered. Both algorithms offer ways to navigate complex solution spaces and embrace uncertainty, making them well-suited for generating a range of potential outcomes in probabilistic prediction scenarios. However, these evolutionary algorithms focused solely on nearby local consistencies during their optimization process [28], resulting in generating single point locations without considering the entire trajectory. The trajectory prediction problems have been revolutionized by Neural Networks (NN) employing deep structures that rely on data loggers that provide extensive and rich datasets [29]. By employing deterministic datasets and architectures, featuring probabilistic activation functions, NN models can be trained to achieve highly accurate predictions of long-term trajectories. A variety of data-driven models, including Logistic Regression (LR), Deep Neural Network (DNN), Support Vector Regression (SVR), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Long-Short Term Memory (LSTM) models, have been effectively utilized as benchmark techniques for ATPs [30]. Regarding the representative neural network for trajectory prediction, hybrid architectures combining transformers and recurrent layers are among the most novel approaches. Combinations of transformers and recurrent layers [31] to leverage the strengths of both architectures have emerged. Transformers excel at capturing global dependencies, while recurrent layers are efficient at modeling temporal patterns. These architectures could significantly improve trajectory prediction performances. While NN ATP models demonstrated strong performance in terms of prediction accuracy, they exhibited vulnerability to adversarial attacks [32]. Adversarial attacks on trajectory prediction models pose serious risks to various applications, including flight safety and operational efficiency. These attacks can exploit the vulnerabilities of neural network models, leading to incorrect predictions that could have far-reaching consequences. It may cause collisions due to airspace congestion, misleading control decisions, operational delays [33]. The ATA's major concern, i.e., safety, was affected by the Adapted Fast Gradient Sign Method (AFGSM) attacks. Furthermore, even with enhanced resilience through adversarial retraining, ATP models continued to be vulnerable to black-box adversarial attacks [34]. Therefore, we conducted a study aimed at devising a resilient algorithm founded on the principles of the Generative Adversarial Network (GAN) methodology [35] to be robust against both white- and black-box adversarial attacks. Generative Adversarial Networks (GANs) offer a unique approach to trajectory prediction, utilizing two components--a generator and discriminator--to generate realistic trajectory predictions. The generator learns from historical data to create synthetic trajectories, while the discriminator evaluates their authenticity. GANs excel in capturing complex patterns and generating diverse, plausible trajectories. They are especially suitable for tasks that involve uncertain outcomes and multiple potential paths. However, challenges such as mode collapse, training instability, and ensuring realism need to be addressed for GANs to reach their full potential in producing accurate and safe trajectory predictions [36]. The Generative Adversarial Network (GAN) is structured around two networks: the \"Generator\" and the \"Discriminator\".