Optimization of GIS analysis using hybrid genetic algorithm

Abstract

Geographic information system (GIS) analysis is used to help in finding solutions for the most important geographical issues. Several programming techniques and methods are used to produce optimal solutions for GIS analysis. One of these techniques is the Solver in Microsoft Excel (MS Excel) which adjusts the values in the Excel sheet cells to produce an optimal result. Hybrid genetic algorithm (HGA) which is a combination of genetic algorithm and Hill-climbing technique is an important optimization method to solve many combinatorial problems such as GIS analysis problems. The solutions provided by HGA are better than the one obtained by any linear programming tool such as the Solver in MS Excel. The Solver produces one solution, which is most of the time not an optimal solution and leads to wrong GIS analysis. In order to prove this idea, several sites are selected as wildlife habitat locations in Lebanon using GIS analysis software, then HGA and the Solver are compared to find the maximum area for a wildlife habitat with the lowest cost of managing the habitat. This comparison proved that HGA finds many optimal solutions for wild life habitat locations better than the solution produced by MS Excel Solver. In addition, the cost provided by HGA is always similar or less than the cost of Solver solution.