A Hybrid Approach Based on ACO and GA for Solving Large Scale Traveling Salesman Problem in GIS

Human has always inspired by his environment to challenge complex issues. This is exposed in many approximation algorithms; from Darvin theory to numerous swarm intelligence procedures. Due to the importance of Traveling Salesman Problem (TSP) in combinatorial optimization, numerous methods are proposed to solve the problem. This paper firstly extends Ant Colony Optimization (ACO) by identifying and optimizing its efficient parameters. Then, a novel method is presented to solve TSP in large scales, based on the improved ACO and Genetic Algorithm (GA) operators. To assess the algorithm, its results are compared with two other procedures, namely ACO and GA in routings between centers of various provinces. It is demonstrated that by using the proposed algorithm, the results have been improved; the running times as well as the necessary storage for saving acquired data in different conditions are reduced. Due to paying attention to the optimum and constant results of the proposed algorithm as well as the importance of improving the services in GIS, the usage of the algorithm in tourism industry is presented.