United Nations outlined 17 Sustainable Development Goals (SDGs), but at the current rate of progress most will not be achieved within the desired timeframe. Since a third of SDGs are directly related to land resources, it is crucial to improve the effectiveness and efficiency of land-use planning. In that regard, there is particular value in algorithmically optimizing land-use planning to better support sustainability. An ideal tool for such optimizations is the nondominated sorting genetic algorithm II (NSGA-II). Improved versions of NSGA-II have been actively developed for land-use problems, but no thorough evaluations and very few comparative studies have been performed. Thus, the objective is to conduct a thorough evaluation of and a systematic comparison between improved NSGA-II algorithms for sustainable land-use optimization. We identified both the most popular and the latest improved algorithms. A theoretical comparison was first made between them in terms of initialization, crossover, mutation, and archiving strategy. Then, a framework consisting of four hierarchal levels (principle, macro-criteria, micro-criteria, and indicators) was developed and applied to make a comprehensive comparison through experiments. The most popular algorithm was demonstrated to produce high-quality results and be computationally efficient, whereas the other performs better in the diversity of results, space efficiency, and the degree of optimization. Both algorithms exhibited excellent performance in handling constraints. Possible approaches to further improve the algorithms include borrowing ideas of scale optimization and gene flow. The proposed framework is capable of guiding further improvement by developers and algorithm selection by users.

中文翻译：

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使用 NSGA-II 的可持续土地利用优化：改进算法的理论和实验比较

联合国概述了 17 项可持续发展目标 (SDG)，但按照目前的进展速度，大多数都无法在预期时间内实现。由于三分之一的可持续发展目标与土地资源直接相关，因此提高土地利用规划的有效性和效率至关重要。在这方面，通过算法优化土地使用规划以更好地支持可持续性具有特别的价值。这种优化的理想工具是非支配排序遗传算法 II (NSGA-II)。已经针对土地使用问题积极开发了 NSGA-II 的改进版本，但没有进行彻底的评估，也很少进行比较研究。因此，目标是对用于可持续土地利用优化的改进 NSGA-II 算法进行彻底评估和系统比较。我们确定了最流行和最新的改进算法。首先从初始化、交叉、变异、归档策略等方面对它们进行了理论比较。然后，开发并应用由四个层次（原则、宏观标准、微观标准和指标）组成的框架，通过实验进行综合比较。最流行的算法被证明可以产生高质量的结果并且计算效率高，而另一个在结果的多样性、空间效率和优化程度方面表现更好。两种算法在处理约束方面都表现出优异的性能。进一步改进算法的可能方法包括借鉴规模优化和基因流的思想。