Urban land use allocation with hybrid linear programming – multi-objective ant colony algorithm

This paper proposes a novel hybrid approach, the Linear Programming-Multi-Objective Ant Colony Algorithm (LP-MOACA), for optimizing urban land use allocation. The LP-MOACA integrates linear programming (LP) with a multi-objective ant colony algorithm (MOACA) to address the challenges of balancing co...

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Bibliographic Details
Published in:Earth science informatics Vol. 18; no. 2; p. 415
Main Authors: Ramyar, Ali, Soltani, Ali, Ramyar, Mohammad, Najafi Kashkooli, Hamed
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2025
Springer Nature B.V
Subjects:
Algorithms
Ant colony optimization
Computational efficiency
Earth and Environmental Science
Earth Sciences
Earth System Sciences
Information Systems Applications (incl.Internet)
Land use
Linear programming
Multiple objective analysis
Ontology
Simulation and Modeling
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
Sustainable development
Urban areas
Urban development
Urban planning
China
LP-MOACA
Land use planning
Optimization
Land use allocation
Meta-Heuristic algorithm
ISSN:1865-0473, 1865-0481
Online Access:Get full text
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Summary:This paper proposes a novel hybrid approach, the Linear Programming-Multi-Objective Ant Colony Algorithm (LP-MOACA), for optimizing urban land use allocation. The LP-MOACA integrates linear programming (LP) with a multi-objective ant colony algorithm (MOACA) to address the challenges of balancing cost minimization, compactness maximization, and land valuation in sustainable urban development. Unlike traditional MOACA, which relies on random initial solutions, LP-MOACA generates high-quality initial solutions through LP, significantly improving computational efficiency and solution quality. The methodology involves rounding LP-derived solutions to binary values, which serve as the starting point for the MOACA process, enabling faster convergence and better optimization of conflicting objectives. The proposed approach is tested in a case study of Kazeroon, Iran, demonstrating a 20% improvement in cost minimization and superior compactness compared to conventional methods. The results show that LP-MOACA outperforms existing models in terms of solution quality, computational efficiency, and its ability to handle complex spatial optimization problems. These findings highlight the potential of LP-MOACA as a powerful tool for urban planners and decision-makers, offering a more sustainable and economically viable approach to land use allocation.
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ISSN:1865-0473
1865-0481
DOI:10.1007/s12145-025-01904-y