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土地利用优化配置是促进土地可持续发展的重要举措,然而现有研究缺乏有效求解土地利用优化配置模型的新型混合式智能优化算法。本文结合蚁群算法和混沌模型,形成混沌蚁群优化(Chaos Ant Colony Optimization,CACO)算法,并以广州市增城区为研究区,对土地利用现状进行优化配置;然后在数量结构、目标函数值、空间布局等方面将优化结果与土地现状及标准蚁群算法优化结果进行对比分析。结果表明:(1)CACO算法能在满足多种约束条件下,有效解决多目标土地利用优化配置问题;(2)与标准蚁群算法相比,CACO算法能增加土地利用的经济效益7.18亿元、生态效益0.33亿元、社会效益1.13%,同时降低地类转换成本1.15%;(3)CACO算法能使土地利用现状空间分布多样性和均匀性的下降控制在1.30%以内,同时缩减地块数量8.86%,并使平均斑块大小增加9.77%,从而提升土地集约利用水平,更合理地配置各现状地类的空间分布,为研究区土地利用的科学规划与决策提供支持。
The optimal allocation of land use is an important measure to promote the sustainable development of land. However, the existing research lacks a new hybrid intelligent optimization algorithm that can effectively solve the optimal allocation model of land use. Based on the ant colony algorithm and the chaos model, this paper forms Chaos Ant Colony Optimization (CACO) algorithm, and uses the Zengcheng District of Guangzhou as the research area to optimize the current situation of land use. Then, the quantitative structure, the objective function value , Spatial layout and other aspects of the optimization results and the status quo of land and standard ant colony optimization algorithm to compare the results. The results show that: (1) The CACO algorithm can effectively solve the problem of multi-objective land use optimization under various constraints; (2) Compared with the standard ant colony algorithm, the CACO algorithm can increase the economic benefits of land use by 718 million yuan , Ecological benefits of 33 million yuan, social benefits of 1.13%, while reducing land conversion costs 1.15%; (3) CACO algorithm can make the spatial distribution of land use diversity and uniformity of the decline control within 1.30%, while reducing the land And the average patch size increased by 9.77%, so as to enhance the level of intensive land use and to rationally allocate the spatial distribution of each landform to provide scientific support for the scientific planning and decision-making of land use.