## 2 Background
### Metaheuristic Methods and Related Works  
The popularity of optimization of machine learning models has significantly increased recently. Various reasons have influence this trend, and the most important two are the complexity of models, which only increases with new achievements, and the second is the growing number of hyperparameters. This process was performed through trial and error, but due to the stated reasons, this is no longer feasible. Considering that parameter selection requires optimal values for both discreet and continuous values, this is considered a mixed NP-hard problem. The main goal is to solve NP-hard problems within reasonable time limits, while maintaining realistic computational requirements. If the parameter selection process is considered an optimization task, the metaheuristic group of algorithms can be applied to provide significant performance increases. Swarm metauristics are a sub-group of such solutions based on cooperative behaviors observed from nature and that have been distinguished by their high performance for the mentioned task. Solutions from this group that are often applied for optimization by researchers include the PSO [17], firefly algorithm (FA) [18], genetic algorithm (GA) [19], Harris hawk optimizer (HHO) [20], artificial bee colony (ABC) [21] algorithm, bat algorithm (BA) [22], and whale optimization algorithm (WOA) [23]. Additionally, the recently introduced chimp optimization algorithms (ChOA) [24] have also been included in this work. An even greater variety of metaheuristic real-world use cases have been recorded in recent years and some are listed in the following text: wireless sensor and IoT optimization [25; 26; 27; 28]; medical image processing and classification [29; 30]; credit card fraud identification [31; 32]; COVID-19 case prediction [33; 34]; fog, cloud, and cloud-edge computing system organization [35; 36; 37; 38]; intrusion detection for network and computer systems [39; 40; 41]; feature selection [42; 43]; the prediction of energy production and consumption [44; 45; 46]; the tuning of different ML structures [47; 48; 49; 50; 51]; and, lastly, tracking and predicting air pollution and environmental monitoring [52; 53; 54]. For the problem of trajectory prediction, many different optimization techniques are applied, and the review presented includes those with and without the application of machine learning techniques, as well as works that included trajectory prediction in general. The research presented in [55] proposed ant-lion optimization of flight trajectory-based LSTM predictions. The results indicated improvements in terms of the convergence speed and accuracy. The authors in [56] applied a PSO-based algorithm to optimize a support vector machine for regression (SVR), used for urban environment vehicle trajectory prediction based on a global positioning system (GPS) and onboard diagnostics (OBD) integration (GOI). SVR parameter selection was the subject of optimization using the swarm metaheuristic. The paper in [57] employed AIS sensor data for SVR model training with adaptive chaos differential evolution. AIS data included the ship's speed, course, time stamp, longitude, and latitude. Optimization was performed for parameter optimization, with the goal of convergence and prediction improvement. A system for early warning in maritime navigation was proposed by [PERSON] et al. [58] with the employment of AIS data, alongside a GRU prediction-based model optimized using a density-based spatial clustering of applications with noise (DBSCAN) algorithm. Vessel trajectories were the prediction object, which was compared to the performance of LSTM on the same problem. The accuracy was near the accuracy of the LSTM, but improvements were noted for the computational cost. In terms of metaheuristic optimization, research topics are various, and the selected examples represent different types of trajectory prediction. In the case of [PERSON] et al. [9], inland water was the predicted space, instead of the open water examples explored in most works.