A Hybrid Fuzzy-IGA-APSO Framework for Real-Time Urban Landscape Optimization in Smart Cities

Abstract

With the rapid development of smart cities, efficient and real-time urban landscape management has become an urgent research topic. This paper proposes a Hybrid Soft Computing Framework (HSCF) that combines Fuzzy Logic, Improved Genetic Algorithm (IGA), and Adaptive Particle Swarm Optimization (APSO) to dynamically optimize urban systems such as lighting and irrigation. By integrating heterogeneous sensor data (e.g., weather, pedestrian flow, and traffic conditions), the framework senses environmental changes and makes optimization decisions in real time.The Fuzzy Logic module handles low-latency adjustments, such as dynamically tuning lighting brightness based on crowd density, achieving response times of less than 100 ms. The IGA performs mid-term optimization of multi-objective landscape layouts (e.g., energy efficiency, aesthetics, and functionality) every 5 minutes, evolving Pareto-optimal solutions through non-dominated sorting and crowding distance analysis with a population size of 50, crossover rate of 0.8–0.95, and mutation rate of 0.05–0.15. The APSO continuously refines these solutions using real-time spatio-temporal data, adaptively balancing exploration and exploitation through inertia weight adjustments (ranging from 0.4 to 0.9) and acceleration constants (c₁ = 1.2–1.8, c₂ = 1.2–2.0).Experimental results demonstrate that HSCF outperforms traditional methods (e.g., FLC and PSO), achieving 16.2%-22.7% energy consumption reduction, 36.6% water savings in irrigation systems, and maintaining stability under extreme weather and ±20% data noise. Key innovations include dynamic spatio-temporal data fusion, real-time decision-making, and joint fitness evaluation across layers. Future work will focus on scalability and integration of additional data sources (e.g., UAV-derived 3D maps) to address more complex urban management tasks. This framework provides a replicable, data-driven solution for adaptive smart city landscape management.