Journal of Geo-information Science >
NSGA Multi-objective Optimization Algorithms and Geographic Decision-making: Principles, State of the Art, and the Future
Received date: 2022-04-22
Revised date: 2022-06-18
Online published: 2023-03-25
Supported by
Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23100303)
Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK0608)
National Natural Science Foundation of China(42171088)
National Natural Science Foundation of China(42171250)
The focus of geography is shifting from qualitative descriptions and quantitative analysis to support decision-making. The process of geographic decision-making usually involves multiple factors to consider and balance to achieve an optimal solution. It is a typical process of multi-objective optimization. Thus, multi-objective optimization algorithms from the field of mathematics are fundamental and have great potential to be applied in geographic decision-making. New algorithms of multi-objective optimization serve as an important source of new methods and tools for geography. This paper reviews a series of Nondominated Sorting Genetic Algorithms (NSGA-I/II/III), which are among the cutting edge and most popular algorithms in the field of multi-objective optimization. This review summarizes the principles, applications, improvements, and problems of these NSGA algorithms. Our findings include: NSGA-II is the most popular algorithm among the series because of its low computational complexity and high usability; NSGA-III has few applications in geographic decision-making for its sophisticated principles; currently, water resource management is the most successful field in applying the NSGA algorithms, and the experiences from this field are of use to others; and land use planning is the most successful field in improving the NSGA algorithms, making the NSGA algorithms more applicable to geographic decision-making. In the future, it is necessary to reduce the difficulty of applying the NSGA algorithms by summarizing typical issues in geographic decision-making and by developing user-friendly software tools for geographers. The efficiency of the NSGA algorithms can be further improved by coupling local searching strategies. It is also recommended to deeply incorporate the NSGA algorithms into the processes of geographic simulations.
GAO Peichao , WANG Haoyu , SONG Changqing , CHENG Changxiu , SHEN Shi . NSGA Multi-objective Optimization Algorithms and Geographic Decision-making: Principles, State of the Art, and the Future[J]. Journal of Geo-information Science, 2023 , 25(1) : 25 -39 . DOI: 10.12082/dqxxkx.2023.220214
表1 NSGA-I、II、III算法的对比Tab. 1 Comparison of NSGA-I, II and III |
算法 | 快速非支配排序 | 解密度的计算方式 | 精英保留 | 时间复杂度 |
---|---|---|---|---|
I | 否 | 适宜度的分享 | 否 | |
II | 是 | 拥挤距离 | 是 | |
III | 是 | 关联至归一化超平面上的参考点 | 是 |
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