Grouping genetic operators for the delineation of functional areas based on spatial interaction

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• 作者：Lucas Martí ; nez-Bernabeu^a ; ^{lucas.martinez@ua.es} ; Francisco Fl&#243 ; rez-Revuelta^b ; ^{florez@dtic.ua.es} ; ; José ; Manuel Casado-Dí ; az^a ; ^{jmcasado@ua.es} ;
• 关键词：Functional areas ; Local market ; Evolutionary algorithm ; Grouping problem ; Regionalisation ; Combinatorial optimisation
• 刊名：Expert Systems with Applications
• 出版年：2012
• 期刊代码：62_09574174
• 类别：et
• 出版时间：15 June, 2012
• 卷：39
• 期：8
• 页码：6754-6766
• 文件大小：608 K

摘要

The delineation of functional economic areas, or market areas, is a problem of high practical relevance, since the delineation of functional sets such as economic areas in the US, Travel-to-Work Areas in the United Kingdom, and their counterparts in other OECD countries are the basis of many statistical operations and policy making decisions at local level. This is a combinatorial optimisation problem defined as the partition of a given set of indivisible spatial units (covering a territory) into regions characterised by being (a) self-contained and (b) cohesive, in terms of spatial interaction data (flows, relationships). Usually, each region must reach a minimum size and self-containment level, and must be continuous. Although these optimisation problems have been typically solved through greedy methods, a recent strand of the literature in this field has been concerned with the use of evolutionary algorithms with ad hoc operators. Although these algorithms have proved to be successful in improving the results of some of the more widely applied official procedures, they are so time consuming that cannot be applied directly to solve real-world problems. In this paper we propose a new set of group-based mutation operators, featuring general operations over disjoint groups, tailored to ensure that all the constraints are respected during the operation to improve efficiency. A comparative analysis of our results with those from previous approaches shows that the proposed algorithm systematically improves them in terms of both quality and processing time, something of crucial relevance since it allows dealing with most large, real-world problems in reasonable time.