Optimization control of urban light art installation LED lighting with infrared sensing based on MBO algorithm

To overcome the limitations of conventional urban light art installations in LED control for interactive experiences and energy management, while simultaneously improving the quality of urban public spaces, this study proposes an optimized LED control method based on the monarch butterfly optimization (MBO) algorithm. A pyroelectric infrared (PIR) sensor array is deployed to construct an infrared sensing network that captures pedestrian interaction parameters, including position and velocity, within the installation environment. These data are fed into an optimized LED control model that balances illumination uniformity and energy efficiency. The enhanced MBO algorithm integrates migration operators, adjustment operators, an opposition-based learning strategy, and adaptive adjustment operators to solve the optimization model, thereby determining the optimal LED dimming levels while preserving interactive performance. Experimental results validate the method’s accurate pedestrian positioning capability, demonstrating a substantial reduction in LED energy consumption, improved illumination uniformity, and dynamic adjustment of lighting power in response to pedestrian density. This method effectively reconciles the demands of interactive experience with the goals of energy optimization in urban light art installations.