Novel Hybrid Optimization Algorithm for Improved Search Performance in Real-World Applications

The paper proposes a new method that combines the advantages of Deep Learning (DL) with the Differential Evolution (DE) algorithm to create a hybrid framework that can solve optimization problems more effectively. The proposed hybrid algorithm enhances the search and optimization procedures by the learning capabilities of DL and the population-based nature of DE algorithm. First, an initial population of solutions is produced by the DE algorithm and put into a DL model for additional refinement. In order to train the DL model—typically a deep neural network—a mix of supervised and unsupervised learning techniques is used. This enables the model to recognize complex relationships and patterns within the challenge space. The combined DE algorithm with DL, as demonstrated by experimental findings for optimum convergence rate and search for addressing complex real-world optimization problems effectively.