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Using an Evolutionary Algorithm in Multiobjective Geographic Analysis for Land Use Allocation and Decision Supporting
Author(s) -
Masoumi Zohreh,
Maleki Jamshid,
Mesgari Mohammad Sadi,
Mansourian Ali
Publication year - 2017
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12111
Subject(s) - variety (cybernetics) , computer science , selection (genetic algorithm) , process (computing) , multi objective optimization , evolutionary algorithm , geographic information system , operations research , function (biology) , mathematical optimization , data mining , artificial intelligence , machine learning , geography , mathematics , remote sensing , evolutionary biology , biology , operating system
Usually, allocation of resources is an optimization problem which involves a variety of conflicting economic, social, and ecological objectives. In such a process, advanced geographic analyst tool for manipulation of spatial data and satisfaction of multiple objectives is essential to the success of decision‐making. The present research intends to demonstrate the application of a multiobjective optimization method based on NSGA‐II[Note 1. Nondominated Sorting Genetic Algorithm‐II. ...] (we call it HNSGA‐II), along with Geographical Information System (GIS)[Note 2. Geographical information system. ...] to select suitable sites for the establishment of large industrial units. Having defined the elements of HNSGA‐II for the site selection of industrial units, the method is tested on the data of Zanjan province, Iran, as the case study. The results showed that the proposed approach can easily find a variety of optimized solutions, giving the decision‐makers the possibility to opt for the most propitious solution. Using this method, the achievement level regarding each objective function can be studied for any of the nondominated solutions.