Research on optimized model of travel route selection based on intelligent image information and ant Colony algorithm

Abstract

The optimum path planning of tourist guides in tourist areas can effectively improve the utilization rate of travel time, in view of the complexity of the path planning problem of tourist attractions, the path of tourist attractions is divided into panoramic and sub-scenic areas, and the same problem is solved. An improved ant colony algorithm combined the intelligent image information is proposed, which designs breeding ants, visual ants and ordinary ants, and all kinds of ants traverse according to their own rules. Ants traverse all scenic spots to find the optimal travel, and update pheromones on the path that meets the requirements according to the tourists flow which is calculated by intelligent image information. Combined with simulated annealing algorithm, ants are traversed in each state. The group travel is rounded and iterated repeatedly to obtain the global optimal solution. The simulation results show that the method has good stability and efficiency in scenic spot path planning, and the algorithm not only makes full use of the positive feedback mechanism to speed up the search, but also enlarges the search area as much as possible so that more edges can form new solutions.