In this study, we address the challenge of unsupervised learning for disentangled representations in datasets including independent variation factors. We propose a new approach inspired from Factor-VAE and $\beta$VAE but integrating the ranger optimizer with dropout layers, which encourages the distribution of representations to be factorial, ensuring independence between dimensions and leading to faster convergence. Our method outperforms Factor-VAE by finding a better balance between disentanglement and reconstruction quality and better optimization of model parameters leading to improved convergence and generalization during learning by effectively adapting the learning rate.