Travel route planning method and system based on microbial genetic algorithm

Abstract

The invention relates to a travel route planning method and system based on a microbial genetic algorithm. The method comprises the steps of constructing a scenic spot database; the user defines a playing area and an interested theme; searching scenic spot data from a scenic spot database according to the requirements of the user; defining population size, chromosome length, iteration times, crossover probability and mutation probability; encoding the scenic spot data and randomly initializing a first-generation population; calculating the fitness of each individual in the population accordingto the fitness function; screening out individuals with the best fitness; if the number of iterations is reached, decoding is carried out to generate an optimal path, planning is ended, otherwise, individuals in the current generation population are paired randomly, and individuals needing to be crossed and mutated are screened; and then a crossover event occurs at a certain probability, a mutation event occurs at a certain probability, and the step of fitness calculation is executed circularly. While reasonable route recommendation is provided, consumption of computing resources is reduced,and the response speed of recommendation results is increased.

Description

technical field [0001] The invention belongs to the field of travel route planning, and in particular relates to a method and system for planning a travel route based on a microbial genetic algorithm. Background technique [0002] At present, with the development of the national economy and the improvement of people's living standards, tourism has become an indispensable activity in people's daily life. Today, with the unprecedented development of the tourism industry, the number of scenic spots planned by the government is also increasing day by day. According to incomplete statistics, there are more than a thousand scenic spots with clear plans in the Beijing area alone. Tourist route planning has become an important service item in the tourism industry. Currently, tourism route planning methods mainly include manual planning and automatic planning using planning algorithms. [0003] Manual planning means that tourism service personnel provide consulting opinions based ...

AI Technical Summary

Problems solved by technology

The characteristic of the precise algorithm is that it can definitely find the global optimal solution, but there are too many nodes traversed, and the amount of calculation is large; the heuristic search algorithm, including A*, D*, Focused D* algorithm, etc., is generally applied to a given starting point and end point. The pathfinding problem is not suitable for the route recommendation problem based on the geographical information data of the scenic spot; the meta-heuristic algorithm includes particle swarm algorithm, simulated annealing algorithm, genetic algorithm, ant colony algorithm, etc., which are characterized by combining random algorithm and local search algorithm. Compared with the exact algorithm, the amount of calculation is less, and at the same time, the optimization process avoids falling into a local optimal solution to a certain extent because of the use of a random algorithm. However, the classic meta-heuristic algorithm also has the problems of large amount of calculation and slow convergence speed.

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