Computational Intelligence based Land-use Allocation Approaches for Mixed Use Areas

2025-08-21

AI Summary

This study addresses the inherent trade-off between land-use compatibility and economic efficiency in mixed-use urban areas. We propose a multi-objective optimization framework to reconcile these competing objectives. Methodologically, we introduce the CR+DES algorithm, which incorporates scaled differential vectors to enhance global search capability, a systematic constraint relaxation strategy to expand the feasible solution space, and a hybrid evolutionary mechanism integrating differential evolution with multi-objective genetic operators (MSBX+MO). Rigorous statistical validation is performed using the Kruskal–Wallis test and compact letter display. Evaluated on a real-world dataset comprising 1,290 parcels, CR+DES improves land-use compatibility by 3.16%, while MSBX+MO increases land-value returns by 3.3%. The framework consistently outperforms state-of-the-art approaches across multiple performance metrics, offering a verifiable, intelligent decision-support tool for sustainable urban land-use policy formulation.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

Urban land-use allocation represents a complex multi-objective optimization problem critical for sustainable urban development policy. This paper presents novel computational intelligence approaches for optimizing land-use allocation in mixed-use areas, addressing inherent trade-offs between land-use compatibility and economic objectives. We develop multiple optimization algorithms, including custom variants integrating differential evolution with multi-objective genetic algorithms. Key contributions include: (1) CR+DES algorithm leveraging scaled difference vectors for enhanced exploration, (2) systematic constraint relaxation strategy improving solution quality while maintaining feasibility, and (3) statistical validation using Kruskal-Wallis tests with compact letter displays. Applied to a real-world case study with 1,290 plots, CR+DES achieves 3.16% improvement in land-use compatibility compared to state-of-the-art methods, while MSBX+MO excels in price optimization with 3.3% improvement. Statistical analysis confirms algorithms incorporating difference vectors significantly outperform traditional approaches across multiple metrics. The constraint relaxation technique enables broader solution space exploration while maintaining practical constraints. These findings provide urban planners and policymakers with evidence-based computational tools for balancing competing objectives in land-use allocation, supporting more effective urban development policies in rapidly urbanizing regions.

Problem

• Optimizing land-use allocation in mixed-use urban areas
• Balancing land-use compatibility with economic objectives
• Developing computational intelligence algorithms for urban planning

Innovation

• Differential evolution with genetic algorithms
• Constraint relaxation strategy for feasibility
• Statistical validation using Kruskal-Wallis tests