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

Peichao Gao; Haoyu Wang; Samuel A. Cushman; Changxiu Cheng; Changqing Song; Sijing Ye

doi:10.1007/s10980-020-01051-3

Treesearch

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

Formally Refereed

Authors:

Peichao Gao, Haoyu Wang, Samuel A. Cushman, Changxiu Cheng, Changqing Song, Sijing Ye

Year:

2020

Type:

Scientific Journal

Station:

Rocky Mountain Research Station

DOI:

https://doi.org/10.1007/s10980-020-01051-3

Source:

Landscape Ecology. 36: 1877-1892.

Download (PDF 1.58 MB)

Download (PDF 1.58 MB): https://research.fs.usda.gov/download/treesearch/64077.pdf

Abstract

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).

Keywords

landscape planning, land-use optimization, sustainable development, sustainable landscape pattern, NSGA-II, Pareto optimal, Pareto front

Citation

Gao, Peichao; Wang, Haoyu; Cushman, Samuel A.; Cheng, Changxiu; Song, Changqing; Ye, Sijing. 2020. Sustainable land-use optimization using NSGA-II: Theoretical and experimental comparisons of improved algorithms. Landscape Ecology. 36: 1877-1892.