基于改进蚁群算法的旅游园区观光路径规划优化

Optimization of Sightseeing Paths in Tourism Park Based on Improved Ant Colony Algorithm

旅游园区智能导航系统的设计一般采用蚁群算法规划路径,综合考虑每个景点的线路距离、地理位置等因素,为不同年龄段的游客推荐不同的观光线路;但经典的蚁群算法在解决路径规划问题上容易陷入局部最优,且存在规划效率低下和收敛速度慢等问题。为此,文章采用经典蚁群算法和遗传算法相结合的改进蚁群算法来提高园区智能导航系统观光路径规划效率。首先,利用遗传算法采用交叉和变异的策略产生寻找最优路径的初始信息素分布;再利用蚁群算法分别对算法中的启发函数、信息素更新机制、状态转移策略进行优化,从而求出旅行商问题最优解,进而优化线路。仿真实验结果显示,在α=1.5、β=3、ρ=0.5、Q=300、重复迭代250次的条件下,改进的蚁群算法在第87次迭代收敛,收敛路径距离为305.62 m;而经典蚁群算法在第196次迭代收敛,收敛距离为519.74 m。可见,改进的蚁群算法解决了经典蚁群算法存在的收敛慢和路线规划距离长等缺陷,在寻找最优解的问题上获得了更高的速率,提高了线路规划的效率。

The intelligent navigation system of tourism parks typically functions to recommend diverse sightseeing paths for tourists of different age groups,comprehensively considering factors such as path distances and the geographical location of all the scenic spots.However,the classical ant colony algorithm often get stuck in local optima during path planning,alongside exhibiting low planning efficiency and a low convergence rate.This paper presents an improved ant colony algorithm combining the classical ant colony algorithm and the genetic algorithm,seeking to enhance the efficiency of the park navigation system in planning sightseeing paths.Initially,the algorithm generates initial pheromone distributions for identifying the optimal path,based on the genetic algorithm and through a strategy of crossover and mutation.Subsequently,the process entails distinct optimizations,utilizing the heuristic function,pheromone update mechanism,and state transition strategy within the ant colony algorithm,ultimately arriving at the optimal solution for the traveling salesman problem(TSP)and further refining paths.The experimental verification showed a path distance of 305.62 meters at the 87th convergence using the improved ACA+GA algorithm,with 250 repeated iterations atα=1.5,β=3,ρ=0.5,Q=300.In contrast,the classical ant colony algorithm resulted in a path distance of 519.74 meters by the 196th convergence.These results showcased the efficacy of the improved ant colony algorithm in overcoming the shortcomings of the classical ant colony algorithm,achieving a higher rate in reaching the optimal solution and an enhanced efficiency of path planning.