Optimization Design of Public Space Functional Area Layout based on Particle Swarm Optimization

Effective design of functional areas in public space is vital for improving spatial use, user satisfaction, and functional operation. Conventional methods of layout planning tend to use static, manual processes or rule-based systems, which are unsuitable for dealing with the dynamic, multi-criteria nature of contemporary spatial needs. These shortfalls lead to inefficient use of space, inadequate flexibility, and inefficiencies in functional area distribution. To solve this problem, this study suggests an intelligent layout optimization framework based on Particle Swarm Optimization (PSO). The PSO algorithm mimics the collective behavior of swarms to find the best configurations that meet spatial, functional, and usability requirements. The suggested model is a structured process that includes data collection, preprocessing, PSO-based optimization, model training, and validation. Experimental performances show that PSO-based scheme attains greater layout efficiency, flexibility, and functional consistency as compared to normal planning methods. The findings indicate that PSO efficiently explores huge design spaces for providing optimal design suggestions. Besides, the novel framework shows extensive adaptability within various spatial contexts and user requests, which enable it to adopt a broad scenario of public planning applications. This research adds a data-driven, scalable solution that enables smarter and more effective public space design, paving the way for future integration into urban planning and smart infrastructure systems.