The Application of Genetic Algorithm in Land Use Optimization Research: A Review

• Published in: Land (Basel) Vol. 10; no. 5; p. 526
• Main Authors: Ding, Xiaoe, Zheng, Minrui, Zheng, Xinqi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2021
• Subjects: Algorithms; bibliometric analysis; Bibliometrics; Citations; evolutionary process; genetic algorithm; Genetic algorithms; Global optimization; Land resources; Land use; land use optimization; Land use planning; Literature reviews; Methods; Neural networks; Optimization; Research facilities; Research institutions; Search algorithms; Software; Sustainable use; Trends; Visualization
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land10050526

Land use optimization (LUO) first considers which types of land use should exist in a certain area, and secondly, how to allocate these land use types to specific land grid units. As an intelligent global optimization search algorithm, the Genetic Algorithm (GA) has been widely used in this field. However, there are no comprehensive reviews concerning the development process for the application of the Genetic Algorithm in land use optimization (GA-LUO). This article used a bibliometric analysis method to explore current state and development trends for GA-LUO from 1154 relevant documents published over the past 25 years from Web of Science. We also displayed a visualization network from the aspects of core authors, research institutions, and highly cited literature. The results show the following: (1) The countries that published the most articles are the United States and China, and the Chinese Academy of Sciences is the research institution that publishes the most articles. (2) The top 10 cited articles focused on describing how to build GA models for multi-objective LUO. (3) According to the number of keywords that appear for the first time in each time period, we divided the process of GA-LUO into four stages: the presentation and improvement of methods stage (1995–2004), the optimization stage (2005–2008), the hybrid application of multiple models stage (2009–2016), and the introduction of the latest method stage (after 2017). Furthermore, future research trends are mainly manifested in integrating together algorithms with GA and deepening existing research results. This review could help researchers know this research domain well and provide effective solutions for land use problems to ensure the sustainable use of land resources.