Path Optimization with Genetic Algoritm (Case Study: Road of Damghan to Dibaj in Semnan County)

Abstract

The existence of a proper road network is one of the factors of economical growth and sustainable development. Traditional routing methods are time-consuming and costly. In addition, the horizontal and vertical components of the route are taken into consideration separately. Since 1970, efforts have been made to automate routing optimization. The Genetic Algorithm is a heuristic method that is used for solving different optimization problems. This research uses the genetic algorithm for path optimization. This algorithm takes both horizontal and vertical dimensions into consideration simultaneously. Chromosomes are defined as a vector array of station points. The suggested method was implemented for the route of Damghan to Dibaj. At first this research explores the importance of the objective functions of the existing route by using an innovative method with an inverse modeling approach. The results show that the share of the length factor is only 10%, so the low degree of the importance of the path length function imposes a lot of cost on the path users, and as a result, it is the main factor of the instability of the existing path. In order to improve the performance, the algorithm parameters were tuned on a simulation region before the final implementation. The objective functions are: route length, technical and engineering, economical, geological and environmental principles. In the final implementation, the algorithm specifies a corridor for the path at the level of the detailed routing. Then in semi-detailed level, the best paths in this corridor will be found. At the end, the optimal alignment is determined at the executive level. Finally the circular arches were implemented automatically based on the Policy and Geometric Design of Highways. By comparing the proposed alignment with the existing road, it shows a reduction in the length of the road by 9.1 km (%18), and 20% less passing than high-cost landuses. The present study shows the high ability of the genetic algorithm in path optimization.