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

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 526
Author(s):  
Xiaoe Ding ◽  
Minrui Zheng ◽  
Xinqi Zheng
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Search Algorithm ◽  
Sustainable Use ◽  
The United States ◽  
Future Research ◽  
Research Institution ◽  
Land Use Optimization ◽  
Optimization Stage ◽  
Optimization Search

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.

Visualizing the History and Perspectives of Disaster Medicine: A Bibliometric Analysis

2019 ◽  
Vol 13 (5-6) ◽  
pp. 966-973 ◽  
Author(s):  
Xinxin Hao ◽  
Yunling Liu ◽  
Xiaoxue Li ◽  
Jingchen Zheng
Keyword(s):  
Bibliometric Analysis ◽  
Growth Period ◽  
The United States ◽  
Disaster Medicine ◽  
Future Research ◽  
Columbia University ◽  
Medicine Research ◽  
Research Institution ◽  
Research Perspectives ◽  
Long Time

ABSTRACTObjective:To analyze the development of disaster medicine and to identify the main obstacles to improving disaster medicine research and application.Methods:A topic search strategy was used to search the Web of Science Core Collection database. The 100 articles with the highest local citation scores were selected for bibliometric analysis; summarizing informetric indicators; and preparing a historiography, themes network, and key word co-occurrence map.Results:The 100 articles with the highest local citation scores were published from 1983 to 2013 in 9 countries, mainly in the United States. The most productive authors were Koenig and Rubinson. The lead research institution was Columbia University. The most commonly cited journal was the Annals of Emergency Medicine. The development of disaster medicine could be separated into 3 consecutive periods. All results indicate that the development of disaster medicine faces some obstacles that need to be addressed.Conclusions:Research works have provided a solid foundation for disaster medicine, but its development has been in a slow growth period for a long time. Obstacles to the development of disaster medicine include the lack of scientist communities, transdisciplinary research, innovative research perspectives, and continuous research. Future research should overcome these obstacles so as to make further advances in this field.

A Genetic Algorithm-Based Quasi-Linear Regression Method and Application

2013 ◽  
Vol 347-350 ◽  
pp. 2700-2705
Author(s):  
Fa Chao Li ◽  
Ke Na Zhang
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Regression Model ◽  
Search Algorithm ◽  
Linear Regression Method ◽  
Computational Mathematics ◽  
Short Term Prediction ◽  
Optimization Search ◽  
Important Branch ◽  
Scientific Prediction

Regression analysis, as an important branch of statistics, is an effective tool for scientific prediction. Genetic algorithm is an optimization search algorithm in computational mathematics. In this paper, a new regression model named quasi-linear regression model is established. Further, its implementation method is introduced in detail. Then by taking the population development of Hebei province as an example, we conduct the fitting problem and short-term prediction. Moreover, we compare the fitting effect and the prediction results of two models.

scholarly journals Optimal reactive power dispatch: a bibliometric analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ibrahim Haruna Shanono ◽  
Aisha Muhammad ◽  
Nor Rul Hasma Abdullah ◽  
Hamdan Daniyal ◽  
Meng Chung Tiong
Keyword(s):  
Visual Analysis ◽  
Reactive Power ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
The United States ◽  
Future Research ◽  
Ieee Transaction ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Reactive Power Dispatch

AbstractOptimal reactive power dispatch (ORPD) plays a significant role in the control and smooth operation of the power system through the enhancement of the network’s reliability, security, and economic aspects. This paper presents a bibliometric and visual analysis of ORPD-related research articles extracted from the Web of Science (WoS) database from its inception to October 29, 2019. A total of 263 articles drawn from 166 journals, published between 1989 and 2019, were retrieved and analysed using Excel, HistCite, and VOSviewer visualisation software. The total number of citations for the 263 articles ranges from 0 to 297. The top three journals with the most significant number of ORPD publications were the International Journal of Electrical Power and Energy Systems, Applied Soft Computing, and two journals qualified for the third place, IEEE Transaction on Power Systems and IET Generation Transmission and Distribution. The most active researcher is Provas Kumar Roy, with nine (9) articles from Kalyani Government Engineering College. The most trending/cited researcher is Yi Jia Cao, with 129 Total Local Citation Scores from Hunan University, Changsha. In terms of contribution by countries, India, China, Iran, and the United States were the most significant contributors with 27.8%, 20.9%, 11.8%, and 8% of the total articles, respectively. The top 5 most frequently used substantive keywords to identify the trending topic and research direction were Particle Swarm Optimisation, Genetic Algorithm, Gravitational Search Algorithm, Linear Programming, Evolutional Algorithm, and Hybrid Algorithm. This study provides a detailed outline and reveals the future research directions for both experienced and novice ORPD researchers to identify research topics, questions, and collaboration partners.

scholarly journals Post-graduation Plans of Undergraduate BME Students: Gender, Self-efficacy, Value, and Identity Beliefs

2020 ◽  
Author(s):  
Anita Patrick ◽  
Maura Borrego ◽  
Catherine Riegle-Crumb
Keyword(s):  
Gender Differences ◽  
Professional Identity ◽  
Graduate School ◽  
Self Efficacy ◽  
Engineering Students ◽  
The United States ◽  
Future Research ◽  
Research Institution ◽  
Hierarchical Agglomerative Cluster ◽  
Academic Self Efficacy

AbstractThis study investigates career intentions and students’ engineering attitudes in BME, with a focus on gender differences. Data from n = 716 undergraduate biomedical engineering students at a large public research institution in the United States were analyzed using hierarchical agglomerative cluster analysis. Results revealed five clusters of intended post-graduation plans: Engineering Job and Graduate School, Any Job, Non-Engineering Job and Graduate School, Any Option, and Any Graduate School. Women were evenly distributed across clusters; there was no evidence of gendered career preferences. The main findings in regard to engineering attitudes reveal significant differences by cluster in interest, attainment value, utility value, and professional identity, but not in academic self-efficacy. Yet, within clusters the only gender differences were women’s lower engineering academic self-efficacy, interest and professional identity compared to men. Implications and areas of future research are discussed.

scholarly journals Educationally Resilient College Students and Their Experiences With Housing Insecurity

2021 ◽  
Author(s):  
◽  
Kathryn Hsieh
Keyword(s):  
Low Income ◽  
Qualitative Interviews ◽  
Well Being ◽  
The United States ◽  
Housing Affordability ◽  
College Completion ◽  
Future Research ◽  
First Generation College Student ◽  
Research Institution ◽  
The Impact

The purpose of this study was to understand how students navigate housing insecurity during their postsecondary experience. Emerging as a recent topic in scholarly discussion, how students address housing affordability and accessibility highlights an important discussion surrounding college opportunity. Qualitative interviews with 20 postsecondary alumni were conducted in a large public research institution in the United States. Through a resilience framework, this study explored how students navigated their housing challenges by leveraging internal and external factors. Housing challenges included living in overcrowded spaces, moving frequently, working significant hours, and reducing monthly expenses such as groceries to ensure housing affordability. The impact of these strategies increased a student's anxiety, negatively affecting their personal well-being and at times their academics. Despite these challenges, participants showed a strong resolve to persevere toward college completion. Themes of self-efficacy (internal) and supportive relationships (external) were important motivators to persist toward college completion in spite of housing challenges and barriers. Each participant was determined to overcome the stigma associated with their housing challenges to increase the social mobility of their family and counter stereotypes associated with being a low-income, minority, or first-generation college student. However, due to the negative perceptions associated with housing insecurity, participants would not disclose the extent of their housing challenges with campus stakeholders. Isolation from these experiences decreased a student's sense of belonging and established a belief that the institution could not provide support to address their housing challenges. Implications for policy, practice, and future research include reassessing financial aid packages, developing direct support offices on campus, and additional opportunities to examine housing insecurity from an identity-based lens.

scholarly journals MULTI-OBJECTIVE LAND USE OPTIMIZATION THROUGH PARALLEL PARTICLE SWARM ALGORITHM: CASE STUDY BABOLDASHT DISTRICT OF ISFAHAN, IRAN

2016 ◽  
Vol 10 (1) ◽  
pp. 42-49
Author(s):  
Alireza Sahebgharani
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Planning ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Multi Objective ◽  
Land Use Optimization ◽  
Land Types ◽  
Better Than

Land use planning seeks to divide land, the most valuable resource in the hands of planners, among different land types. During this process, various conflicting objectives are emerged which land use planners should prepare land use plans satisfying these objectives and deal with a large set of data and variable. For this reason, land use allocation is a multi-objective NP-hard optimization problem which is not solvable by the current exact methods. Therefore, solving land use optimization problem relies on the application of meta-heuristics. In this paper, a novel meta-heuristic named parallel particle swarm is developed to allocate seven land types (residential, commercial, cultural, educational, medical, sportive and green space) to Baboldasht district of Isfahan covered by 200 allocation cells with size 1000 m2 for maximizing compactness, compatibility and suitability objective functions. Afterwards, the outputs of the new developed algorithm are compared to the outputs of genetic algorithm. The results demonstrated that the parallel particle swarm is better than genetic algorithm in terms of both solution quality (1.35%) and algorithm efficiency (63.7%). The results also showed that the outputs achieved by both algorithms are better than the current state of land use distribution. Thus, the method represented in this paper can be used as a useful tool in the hands of urban planners and decision makers, and supports the land use planning process.

scholarly journals Spatial Multi-Objective Land Use Optimization toward Livability Based on Boundary-Based Genetic Algorithm: A Case Study in Singapore

2020 ◽  
Vol 9 (1) ◽  
pp. 40 ◽  
Author(s):  
Kai Cao ◽  
Muyang Liu ◽  
Shu Wang ◽  
Mengqi Liu ◽  
Wenting Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Central Region ◽  
Optimization Model ◽  
Optimization Problem ◽  
Weighted Sum ◽  
Multi Objective ◽  
Land Use Optimization ◽  
Future Direction

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have been successfully conducted, which can demonstrate the effectiveness of the spatial multi-objective land use optimization model developed in this research as well as the robustness and reliability of computer-generated solutions. In addition, the comparison between the computer-generated solutions and the two real planned scenarios has also clearly demonstrated that our generated solutions are much better in terms of fitness values. Lastly, the limitation and future direction of this research have been discussed.

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