Optimization and control of light and shadow effects in environmental art design based on particle swarm algorithm

Abstract

Traditional methods for optimizing light and shadow effects in environmental art design struggle to meet the demands of complex and dynamic design environments, often facing issues such as high computational complexity, low efficiency, and limited optimization outcomes. To address these challenges, this study introduces an improved particle swarm optimization (IPSO) algorithm for optimizing light and shadow effects. The key parameters—such as position, intensity, and angle—of the light source configuration are treated as particle state variables. By incorporating dynamic inertia weights and adaptive learning factors, the algorithm’s global search capability and local convergence performance are significantly enhanced. A comprehensive fitness function, which takes into account both the esthetic and functional aspects of light and shadow, is defined to guide the IPSO toward finding the optimal configuration. Through steps such as particle initialization, speed and position updates, and dynamic parameter adjustments, IPSO efficiently and rapidly optimizes light and shadow effects. Experimental results demonstrate that, after optimization, the light intensity of each source varies between 140 lux and 160 lux under different light source positions, improving light uniformity. Additionally, the smoothness of shadow transitions in the interior exhibition hall design is enhanced by 44.3%. This research marks a significant advancement in the accuracy and efficiency of light and shadow optimization in environmental art design, offering an effective solution for addressing the complexities of modern design challenges. The proposed approach not only improves design outcomes but also provides a more efficient methodology for handling intricate design scenarios, making it a valuable tool for practitioners in the field.