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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年5月23日

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  • Ali Ramyar,
  • Ali Soltani,
  • Mohammad Ramyar,
  • Hamed Najafi Kashkooli

摘要

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.