Journal of Engineering, Project, and Production Management, 2026, 16(3), 2025-197
Urban Landscape Design Optimization Using Multi-Objective Particle Swarm Algorithms
Lecturer, College of Art and Design, Communication University of China, Nanjing, 211172, China, E-mail: li152qingxuan@163.com
Project Management
Received September 4, 2025; revised October 23, 2025; accepted October 27, 2025
Available online April 8, 2026
Abstract: Optimizing land use in urban landscape design can improve the efficiency of urban land use, enhance the attractiveness of urban landscape, and guarantee the ecological and economic benefits for the city. However, many current urban landscape design methods still suffer from low land utilization and poor urban landscape coordination. To solve these problems, this study combines a multi-objective particle swarm algorithm with a Multi-Objective Optimization Problem (MOP). It proposes an innovative urban landscape design method by constructing and solving a multi-objective optimization model for landscape design and land use. The study analyzes the practical effects of the landscape design method. The results indicated that the method increased the urban land utilization rate from 78.3% to 93.7% and the forest coverage rate from 34.2% to 47.6%. Moreover, the city’s ecological environment and landscape coordination scores both increased to more than 90 points. Public satisfaction with the landscape design also increased, reaching 92.7%. In summary, the proposed urban landscape design method improves the urban land resource utilization rate while also ensuring landscape coordination and high-quality urban ecological environment. It can also provide urban landscape design references for urban planners.
Keywords: Urban landscape design, land use, optimization model for multi-objective problems, multi-objective particle swarm optimization.
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Citation: Li, Q. (2026). Urban Landscape Design Optimization Using Multi-Objective Particle Swarm Algorithms. Journal of Engineering, Project, and Production Management, 16(3), 2025-197.
