A novel approach for automated land partitioning using genetic algorithm |
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Affiliation: | 1. Department of Industrial Management, University of Seville, Spain;2. Engineering School, University of Oviedo, Spain;1. LERIA, Université d’Angers, 2 Boulevard Lavoisier, 49045 Angers, France;2. Institut Universitaire de France, 1 rue Descartes, 75231 Paris, France;3. School of Management, Northwestern Polytechnical University, 127 Youyi West Road, 710072 Xi’an, China;1. DEI, University of Padua, viale Gradenigo 6, Padua, Italy;2. BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, BioMediTech, Tampere, Finland;3. Computer Information Systems, Missouri State University, 901 S. National, Springfield, MO 65804, USA;1. National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China;2. Chongqing Three Gorges College, Chongqing, 404000, China |
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Abstract: | Land consolidation is an important tool to prevent land fragmentation and enhance agricultural productivity. Land partitioning is one of the most significant problems within the land consolidation process. This process is related to the subdivision of a block having non-uniform geometric shapes. Land partitioning determines the location of new land parcels and is a complex problem containing many conflicting demands, so conventional programming techniques are not sufficient for this NP optimization problem. Therefore, it is necessary to have an intelligent system with a standard decision-making mechanism capable of processing many criteria simultaneously and evaluating a number of different solutions in a short time. To overcome this problem and accelerate the land partitioning process, we proposed automated land partitioning using a genetic algorithm (ALP-GA). Besides the parcel's size, shape and land value, the proposed method evaluates fixed facilities, and the degree and location of cadastral parcels to generate a land partitioning plan. The proposed method automated the land partitioning process using an intelligent system and was implemented over a real project area. Experimental study shows that the proposed method is more successful and efficient than the designer with respect to the results meeting the objective function. In addition, the land partition process is greatly simplified by the proposed method. |
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