5A Scenic Spot Tourism Route Planning Based On Genetic Simulated Ant Colony Algorithm

In recent years, with the development of economy, the tourism industry occupies an important part in the global economic structure, and the state has gradually evaluated some scenic spots with a large number of tourists and a relatively deep cultural heritage as 5A scenic spots. Since 2007, the country has 279 tourist attractions were assessed as 5 A scenic spots, including 13 scenic spots in Henan Province,. But because the individual scenic area was assessed late and local traffic situation is poor, so the number of tourist scenic spots and tourist cost less, and maintenance is relatively high, which caused a certain influence on the national economy and the level of the local economy. In order to make some depressed scenic spots active, and reduce the economic losses caused by tourism congestion, it is necessary to adjust and plan the model of 5A scenic spot tourism route.In this paper, according to the domestic and foreign scholars on the study of the traveling salesman (TSP) problem, a detailed reference to a large number of references,and the data of the 5A scenic spot in Henan province were collected and studied. Based on the shortest path model which satisfies the constraints while using genetic algorithm and ant colony algorithm respectively to optimize the program, get to meet the conditions of the shortest path planning model, finally the travel route map and travel distance. The design ideas of this paper are as follows:(1)First of all, the mathematical model is established through the constraints in the travel route, and the constraint conditions are mainly used in the route to reach the scenic area i of the total amount of quanti so that the constraints are met:quantj? quanti + piaojiaj+lufeiij- cap+cap· precij+ (cap - piaojiaj-piaojiai-lufeiij-lufeiji) · precjiOn this basis, the 0-1 variable precij is generated in the travel route to get the length of the optimal path.(2) Secondly, the genetic algorithm mainly uses the integer sorting number, the scenic area as the chromosomes, each chromosome corresponds to a number of city, and then the encoding of chromosomes to generate the shortest path to meet on chromosome.The chromosomes of the initial population are selected, crossed, transformed and transformed, and then the new individuals under the genetic operation objv are decoded.After solving the genetic algorithm to find the 5A scenic spots in Henan shortest path length of 1283km .(3)finally, the shortest path problem using ant colony algorithm for solving the constrained conditions, mainly using the method of random search, let each ant in the path pheromone release, and other perceived path pheromone, which represents the path pheromone more shorter, so the ants with high probability selection a high level of pheromone path and release a pheromone, in order to increase the concentration of pheromone in the path,it will form a positive feedback,so that the ants finally can find a path to achieve the optimal solution of objective function value objv . The number of scenic spots visited by ants is stored in the named variable Tabu . So the ant colony algorithm, tourism spending limit of 1000 yuan limit based on a total of three alternative paths,the shortest path and the three path generation for 1568 km,a total amount of 2055 yuan in tourism consumption.