Improving the computational efficiency of highway alignment optimization models through a stepwise genetic algorithms approach

In this paper we propose a stepwise genetic algorithms approach for optimizing highway alignments for improving computational efficiency and quality of solutions. Our previous work in highway alignment optimization has demonstrated that computational burden is a significant issue when working with a geographic information system (GIS) database requiring numerous spatial analyses. For solving real-world problems working directly with real maps through a GIS is highly desirable. Furthermore, saving computation time can enhance adoptability of a model especially when a study area is relatively large, or involves many sensitive properties, or if locating complex structures such as intersections, bridges and tunnels is necessary. It is well acknowledged that in many optimization processes subdividing large problems into smaller pieces can decrease the computation time and produce a better solution. In this research two different population sizes are used to develop a stepwise alignment optimization when employing genetic algorithms in suitably subdivided study areas. An example study shows that the proposed stepwise optimization gives more efficient results than the existing methods and also improves quality of solutions.