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Sustainable land-use optimization using NSGA-II: theoretical and experimental comparisons of improved algorithms

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Abstract

Context

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).

Objectives

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.

Methods

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.

Results

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.

Conclusions

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.

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Acknowledgments

This research was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (Grant No. 2019QZKK0608), National Natural Science Foundation of China (Grant No. 41901316, 41771537, and 41801300), State Key Laboratory of Earth Surface Proscesses and Resource Ecology (Grant No. 2020-KF-03), and the Fundamental Research Funds for the Central Universities (Grant No. 2019NTST02). We would like to thank the high-performance computing support from the Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University.

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Authors and Affiliations

  1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

    Peichao Gao

  2. Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China

    Peichao Gao, Haoyu Wang, Changxiu Cheng, Changqing Song & Sijing Ye

  3. Rocky Mountain Research Station, USDA Forest Service, 2500 S Pine Knoll Dr, Flagstaff, AZ, 86004, USA

    Samuel A. Cushman

  4. National Tibetan Plateau Data Center, Beijing, 100101, China

    Changxiu Cheng

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  1. Peichao Gao
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  2. Haoyu Wang
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Correspondence to Changxiu Cheng or Changqing Song.

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Gao, P., Wang, H., Cushman, S.A. et al. Sustainable land-use optimization using NSGA-II: theoretical and experimental comparisons of improved algorithms. Landscape Ecol 36, 1877–1892 (2021). https://doi.org/10.1007/s10980-020-01051-3

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