基于改进混合蛙跳算法的个性化旅游路线推荐

大众在旅游途中期望获得开销低、行程方便、舒适度高的旅游体验，同时还具有历史人文、自然景观、美食购物等不同游览需求.因此，本文提出了一种基于改进混合蛙跳算法的个性化旅游路线推荐方法.首先建立个性化旅游路线推荐问题的优化模型，并针对该模型的特点，设计改进混合蛙跳算法.通过调整可控精度，增加筛选准则和及时处理异常解等策略增强群体的多样性，降低遗漏最优解的风险，强化局部搜索能力，并提高算法的求解精度.以南京三日游个性化旅游路线推荐问题作为实例，收集南京市内知名景点的门票价格、开放时间、不同出行方式所需的时间和花费情况以及食宿费用等相关数据，基于改进混合蛙跳算法进行求解.实验结果表明，与改进前的方法相比，所提改进方法能够获取更优的路径解，推荐的路线能够更好地满足用户的个性需求.

Personalized travel route recommendation based on an improved shuffled frog leaping algorithm

Tourists expect to get travel experiences like low-cost, convenient itinerary, high comfort and so on.Meanwhile, they have different tourist interests such as history and culture, natural landscape, food and shopping, etc.Therefore, a personalized travel route recommendation method based on an improved shuffled frog leaping algorithm is proposed in this paper.A model is established to optimize the personalized travel route recommendation problem, and an improved shuffled frog leaping algorithm is designed based on the characteristics of the model.By adjusting the controllable accuracy, incorporating new selection criteria and handling abnormal solutions in time, the population diversity is increased and the risk of missing the optimal solution is reduced, which enhance the local search ability and searching accuracy of the algorithm.The personalized travel route recommendation for three-day tour in Nanjing is taken as an instance to verify the proposed method.Relevant data are collected, including admission fees, opening hours, and accommodation expenses for well-known tourist attractions in Nanjing.The results show that compared with basic shuffled frog leaping algorithm, the proposed method can recommend touring routes with higher accuracy and better meet the users'' individual interests.