Abstract

Currently, technologies such as convolutional neural network (CNNs) and deep Q network (DQNs) are undergoing intensive research and rapid development, driving vigorous advancement in the field of artificial intelligence. Nevertheless, there is still room for improvement in addressing practical industrial problems and enhancing learning efficiency and accuracy. To address the core challenges of traditional evolutionary algorithms (EAs) in complex optimization problems, such as insufficient scalability, limited environmental adaptability, and low computational efficiency, this paper proposes an evolutionary algorithm optimization framework (DLRL-EAF) that fuses deep learning and reinforcement learning. To verify the effectiveness of the proposed method, six standard test functions (Sphere, Rastrigin, Griewank, etc.) and three practical engineering optimization problems (mechanical parts design, logistics path planning, photovoltaic array layout) are selected for comparison experiments. The performance of DLRL-EAF is evaluated using the standard genetic algorithm (SGA), the particle swarm optimization algorithm (PSO), and the adaptive genetic algorithm (AGA). Experimental results show that DLRLEAF improves the accuracy of optimal solutions by an average of 23.6%, accelerates iterative convergence by 31.2%, demonstrates greater stability in high-dimensional, complex problems, and improves scalability by more than 40%. At the same time, the proposed method significantly reduces the time and resource costs of problem-solving in practical engineering applications and demonstrates its practical value in industrial settings.OPEN ACCESS Received: 27/12/2025 Accepted: 26/03/2026