Touring time optimization method for tourist attractions

Abstract

The invention discloses a touring time optimization method for tourist attractions and designs a shortest touring time method applicable to the tourist attractions to help tourists to realize a high-efficiency tourist attraction touring way so that more tourist attractions can be toured in the same time. The method realizes the optimization of total touring time for touring the tourist attractions to be toured by the tourists and also enables service loads including traffic, hotels, repasts, shops and the like in the tourist attractions to be reasonable and balanced. An algorithm combines a particle swarm optimization and a genetic algorithm and improves a problem of a smallest physical distance for traversing all cities, which is discussed by a common travelling salesman problem; the size of a time fitness value determines the updating of particles and the iteration of swarms, wherein time of the time fitness value is jointly determined by the physical distance between the tourist attractions, the time needed for touring each tourist attraction and waiting time needed for touring each tourist attraction under a real-time condition. The algorithm can better combine practical conditions to provide better path push services to the tourists.

Description

technical field [0001] The invention relates to the field of travel information services, in particular to a travel time optimization method for tourist attractions. Background technique [0002] With the continuous improvement of contemporary people's living standards, people's requirements for quality of life have also increased. In recent years, people's demand for cultural tourism has been continuously strengthened. At the same time, the requirements for advanced computer information science and technology in the tourism process are gradually increasing. Technologies such as the Internet of Things, indoor and outdoor mixed positioning, GIS geographic information map construction, user handheld terminal software development, and 3D playback equipment have already matured or are basically mature. The application of these technologies into the reality of cultural tourism shows that The unity of culture, tourism and technology. [0003] Existing path selection techniques in...

AI Technical Summary

Problems solved by technology

The ant colony algorithm has many parameters, generally requires a long search time, and there will be a stagnation phenomenon, that is, after the search reaches a certain level, the solutions found by all individuals are exactly the same, and the solution space cannot be further searched, which is not conducive to finding more good solution

The simulated annealing algorithm requires a higher initial temperature, a slower cooling rate, a lower termination temperature, and enough sampling at each temperature, so its convergence speed is slow and the execution time is long, and the performance of the algorithm is related to the initial value and Parameter sensitivity and other shortcomings

In addition, if the cooling process is too fast, the global optimal solution may not be obtained.

[0004] Most of the existing scenic spot path planning algorithms are based on the basic TSP problem, and the path with the shortest physical distance is obtained. Only a small part considers the problem of reducing the tour time and improving the tour efficiency, which is not closely integrated with the actual situation.

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