Intelligent Optimization Algorithm for Chain Restaurant Spatial Layout Based on Generative Adversarial Networks

This research presents an intelligent optimization algorithm framework for chain restaurant spatial layout generation based on Generative Adversarial Networks (GANs). Contemporary restaurant design methodologies rely on subjective expertise and static planning approaches that inadequately address dynamic operational requirements and evolving consumer preferences. The proposed GAN-based architecture incorporates a dual-generator framework with progressive upsampling modules and multi-head attention mechanisms specifically designed for restaurant spatial optimization. The multi-objective optimization function integrates operational efficiency metrics, spatial utilization coefficients, and aesthetic quality assessments through weighted objective aggregation, achieving balanced performance across competing design criteria. Experimental validation utilizing 3,892 restaurant layouts across 47 chain brands demonstrates substantial improvements in spatial layout quality metrics. Generated layouts achieve average efficiency scores of 87.3% compared to traditional baseline measurements of 72.8%, representing a 19.9% performance enhancement. The algorithm reduces average customer movement distances by 23.4% while maintaining 92.6% regulatory compliance rates. Implementation case studies across three distinct restaurant chains validate practical deployment feasibility with measurable improvements in operational efficiency ranging from 12.9% to 24.6%. The research establishes foundational technologies for next-generation intelligent restaurant design systems that enable data-driven optimization while reducing traditional design development timelines by approximately 65%. Commercial deployment analysis indicates potential cost savings of $12,000-$18,000 per location through reduced architectural consultation requirements.