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Fuzzy C-Means Clustering and Particle Swarm Optimization based scheme for Common Service Center location allocation

机译:基于普通服务中心位置分配的模糊C-Means聚类基于粒子群优化方案

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摘要

Common Service Centers (CSCs), which are also known as Tele-centers and Rural Kiosks, are important infrastructural options for any country aiming to provide E-Governance services in rural regions. Their main objective is to provide adequate information and services to a country's rural areas, thereby increasing government-citizen connectivity. Within developing nations, such as India, many CSC allocations are being planned. This study proposes a solution for allocating a CSC for villages in a country according to their E-Governance plan. The Fuzzy C-Means (FCM) algorithm was used for clustering the village dataset and finding a cluster center for CSC allocation, and the Particle Swarm Optimization (PSO) algorithm was used for further optimizing the results obtained from the FCM algorithm based on population. In the context of other studies addressing similar issues, this study highlights the practical implementation of location modeling and analysis. An extensive analysis of the results obtained using a village dataset from India including four prominent states shows that the proposed solution reduces the average traveling costs of villagers by an average of 33 % compared with those of allocating these CSCs randomly in a sorted order and by an average of 11 % relative to centroid allocation using the FCM-based approach only. As compared to traditional approaches like P-Center and P-Median, the proposed scheme is better by 31 % and 14 %, respectively. Therefore, the proposed algorithm yields better results than classical FCM and other types of computing techniques, such as random search & linear programming. This scheme could be useful for government departments managing the allocation of CSCs in various regions. This work should also be useful for researchers optimizing the location allocation schemes used for various applications worldwide.
机译:作为远程中心和农村信息亭的共同服务中心(CSC)是任何旨在为农村地区提供电子治理服务的国家的重要基础设施选择。他们的主要目标是为一个国家的农村地区提供足够的信息和服务,从而增加政府公民的连接。在发展中国家,如印度,正在计划许多CSC分配。本研究提出了根据其电子治理计划在一个国家为一个国家分配CSC的解决方案。模糊C型算法用于聚类村数据集并找到CSC分配的集群中心,并且使用粒子群优化(PSO)算法用于进一步优化基于群体的FCM算法获得的结果。在解决类似问题的其他研究的背景下,本研究突出了位置建模和分析的实际实施。广泛分析了使用来自印度的村庄数据集获得的结果,包括四个突出状态,表明,与随机分配的顺序随机分配这些CSC,案件的平均旅行成本降低了33%的平均行驶成本,平均为33%。仅使用基于FCM的方法的质心分配平均11%。与P-Center和P中位等传统方法相比,所提出的方案分别较好31%和14%。因此,所提出的算法产生的结果优于经典FCM和其他类型的计算技术,例如随机搜索和线性编程。该计划可用于政府部门管理各地区CSC的分配。这项工作也适用于研究人员优化全球各种应用的位置分配方案。

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