Path optimization model of smart elderly care service based on reinforcement learning and particle swarm optimization

To enhance the efficiency and adaptability of path planning in smart elderly care services, this paper proposes a hybrid path optimization model integrating Deep Q-Network (DQN) and Particle Swarm Optimization (PSO). Traditional approaches struggle with dynamic environmental adaptation and multi-objective optimization, often leading to suboptimal routing. To address this, the proposed model employs DQN to establish an autonomous decision-making framework, leveraging reinforcement learning to dynamically optimize path selection based on environmental variations and reward mechanisms. Meanwhile, PSO enhances the global search capability, adjusting particle positions and velocities to mitigate the risk of local optima and improve overall path efficiency. A multi-objective optimization framework is further introduced, incorporating weighted coefficients to balance competing objectives and ensure comprehensive optimization. Additionally, a dynamic environmental adaptation mechanism enables real-time data updates, allowing the system to swiftly respond to sudden environmental changes and continuously refine path selection. Experimental results demonstrate the model’s superior performance, achieving an average path planning time of 52 seconds and a service response time of 17 s. The approach maintains high path accuracy, with a minimal distance deviation of 1.35%, and delivers an optimized objective function value of 0.84. These findings highlight the model’s effectiveness in real-time adaptive path planning, offering a robust and intelligent solution for elderly care mobility and service optimization.