  Weather forecasting plays a vital role in numerous sectors, but accurately
capturing the complex dynamics of weather systems remains a challenge for
traditional statistical models. Apart from Auto Regressive time forecasting
models like ARIMA, deep learning techniques (Vanilla ANNs, LSTM and GRU
networks), have shown promise in improving forecasting accuracy by capturing
temporal dependencies. This paper explores the application of metaheuristic
algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and
Particle Swarm Optimization (PSO), to automate the search for optimal
hyperparameters in these model architectures. Metaheuristic algorithms excel in
global optimization, offering robustness, versatility, and scalability in
handling non-linear problems. We present a comparative analysis of different
model architectures integrated with metaheuristic optimization, evaluating
their performance in weather forecasting based on metrics such as Mean Squared
Error (MSE) and Mean Absolute Percentage Error (MAPE). The results demonstrate
the potential of metaheuristic algorithms in enhancing weather forecasting
accuracy \& helps in determining the optimal set of hyper-parameters for each
model. The paper underscores the importance of harnessing advanced optimization
techniques to select the most suitable metaheuristic algorithm for the given
weather forecasting task.
