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The application of ant colony optimization algorithm in tour route planning
Abstract
Traditional tour route planning mostly depends on the planner's experience. However, whether tour route planning is appropriate or not has a considerable impact on the time and cost of the tour. Good planning can save unnecessary waiting time, avoid wasted operating costs, and contribute to the enhancement of the quality of tourism. Therefore, this study applied the ant colony optimization algorithm to build a tour route planning model. As the empirical results have shown, the proposed tour route planning model can effectively and rapidly complete route planning and achieve the objectives of optimum route and minimum cost.
... Therefore, through the ant colony behavior, ants continuously learn and optimize through the information feedback mechanism to determine the shortest foraging path. According to the characteristics of ACO algorithm, it has been widely used in path planning [19], network routing [20], logistics distribution [21], trip route planning [22] and traveling salesman problem [6,23]. ...
... Traditional tourism route planning methods introduce some heuristic algorithms to solve the optimal path, and achieves good results. Huanwg introduced the chance algorithm into ACO, which has good effect in dynamic tourism route planning [22]. Mei took time as the key constraint, and gave a tourism route planning prototype combined with ant colony algorithm [44]. ...
To solve the problem of one-sided pursuit of the shortest distance but ignoring the tourist experience in the process of tourism route planning, an improved ant colony optimization algorithm is proposed for tourism route planning. Contextual information of scenic spots significantly effect people’s choice of tourism destination, so the pheromone update strategy is combined with the contextual information such as weather and comfort degree of the scenic spot in the process of searching the global optimal route, so that the pheromone update tends to the path suitable for tourists. At the same time, in order to avoid falling into local optimization, the sub-path support degree is introduced. The experimental results show that the optimized tourism route has greatly improved the tourist experience, the route distance is shortened by 20.5% and the convergence speed is increased by 21.2% compared with the basic algorithm, which proves that the improved algorithm is notably effective.
... has proposed the solution of ant colony algorithm in the application of travel route planning, they discussed the application in vehicle routing problem based on ant colony algorithm and completed the travel of 201 5A scenic spots in the country that using the shortest time. However, this paper does not study the planning and generation of travel routes that meet needs according to user's preference conditions [5]. Amazon's G Linden [9] and others proposed an item-based collaborative filtering algorithm, which is well suited for comparing similar items rather than comparing similar users. ...
With the rapid development of China’s economy and the increase in tourism consumption, the number of people in traveling in domestic tourism has increased rapidly each year, and more travelers choose privately customized travel routes, so reasonable travel route is generated based on the actual users’ needs has become a hot research spot in the current industry and academia. However, as far as practical application is concerned, the planning of travel routes is a comprehensive and complex task. Reasonable travel routes include comprehensive features such as reasonable travel cities, travel time, transportation methods, and itinerary arrangements. At present, the traditional method is basically that the customer manager can manually plan the suitable travel route for the user through collecting the user’s needs, and then modify and adjust by communicating with the customer. The problem that this brings is that the customer manager needs to compare information such as users’ needs, travel price, travel time, travel transportation, and scenic spot arrangements when planning numerous travel routes. Obviously, the traditional methods have significant disadvantages such as low efficiency and long time-consuming. Bring a great burden to the staff and it is incompatible with the development of the current industry.
In order to solve the above problems, we put the historical travel routes collected as data sets in the paper, and a travel route recommendation and generation algorithm based on LDA and collaborative filtering is designed. Reasonable city recommendation list and playing time are the basis and focus of route planning. The paper is based on the many shortcomings in the traditional travel route planning method, and takes the city’s recommendation and time planning as the main focuses on work. In this work, different recommendation algorithms were designed, including a recommendation algorithm based on Latent Dirichlet Allocation (LDA) and collaborative filtering. By analyzing the performance of the recommendation algorithm on the data sets, the recommendation algorithm is improved and optimized. The LDA algorithm based on KDE (Kernel Density Estimation) and classification, the collaborative filtering algorithm based on KDE and classification. The final experimental results show that the optimal city list and travel time generated by the recommended algorithm are more reasonable and satisfy the actual use of the user.
... In our ACO cluster algorithm, the probability of sample x to be assigned to cluster C i will be [15] , , ...
Clustering analysis is an important field in data mining, and also one of the current research hotspots in computer science. This paper focus on some classical data clustering algorithms and swarm intelligence, especially ant colony optimization, trying to combine these two kinds of algorithms and improve the efficiency and accuracy of data clustering. This paper proposes a new ant colony optimization data clustering algorithm, named ant colony clustering algorithm with elitist ant and local search (ACC-EAL). This algorithm adopts a new pheromone incremental calculation method, making the distances among the clusters tend to increase, and the clusters get denser. Meanwhile local search provides the ants more opportunity to find optimal solution and the elite ant strategy makes the ants with optimal solutions contribute more to the pheromone increment.
Optimal travel route selection in real time basis minimizes the transportation cost for the tourists and guarantees the ease of travel as well. Accordingly, this paper deals with the problem of selecting the appropriate route selection to visit the identified tourists' spots in Tripura, India using the vehicle routing problem with time windows. The different tourist spots in the study area are identified from the Tourism Department of the Government of Tripura. This study considers only two cities as the gateway to reach to Tripura. After which, based on Bing map portal, geographic information for all the selected tourist spots is mapped; and one to one distance matrix is prepared considering the identified tourist spots and the two-gateway point of Tripura. The result so derived is compared and contrasted with the traditional route followed by the travel operators of Tripura; and found significant differences in terms of cost of transportation and time spent to visit the different tourist spots in Tripura. Finally, this paper concluded that selection of optimal route for transport network of tourism spots in Tripura, India using multi-trip vehicle routing problem can ensure the ease of travel and satisfaction for the tourists visiting in Tripura, India.
The paper presents an innovative method for tourist route planning inside a destination. The necessity of reorganizing the tourist routes within a destination comes as an immediate response to the Covid-19 crisis. The implementation of the method inside tourist destinations can be an important advantage in transforming a destination into a safer destination in times of Covid-19 and post-Covid-19. The existing trend of shortening the tourist stay length has been accelerated while the epidemic became a pandemic. Moreover, the wariness for future pandemics has brought to the spotlight the issue of overcrowded attractions inside a destination at certain moments. The method proposed in this paper proposes a backtracking algorithm, more precisely an adaptation of the travelling salesman problem. The method presented aims to facilitate the navigation inside a destination and to revive certain less-visited sightseeing spots inside a destination while facilitating the social distancing measures imposed by Covid-19.
Independent travelers, especially professional independent travelers, tend to plan their trip schedules according to their interests, preferred hotels, landmarks they wish to visit, budgets, time availability and various other factors. Hence, travel schedule planning is valuable for satisfying the unique needs of each traveler. In this paper, we propose an algorithm for independent travel recommendation, consisting of three steps. Firstly, landmarks in the destination are selected under the specific constraints, which is modeled as a 0-1 knapsack problem. Then, the landmarks will be evaluated comprehensively using AHP (Analytic Hierarchy Process) model, and the greedy simulated annealing algorithm is adopted to select the best landmarks with high evaluation scores. Next, with AHP-decision model, a most reasonable free line to the tourist destination is selected from multiple candidates. Lastly, the path planning among the landmarks is abstracted as a TSP (Travelling Sales Problem) problem, and the simulated annealing algorithm based on roulette wheel selection is adopted to solve it. Through simulation experiments, by comparing with package tour from the aspects of landmark selection, valid sightseeing time ratio, valid sightseeing consumption ratio and the tourist satisfaction, the proposed algorithm is evaluated and analyzed. Simulation results illustrate the feasibility and rationality of our approach, which can be used as an effective reference deciding individualized travel schedules and trip planning.
This study proposes a rank-based ant colony optimization (ACO) method with a rank-based nonlinear selective pressure function and a modified Q-learning method to enhance the convergence characteristics of original ACO Dorigo et al., which defines the probability of exploring a city to be visited by ants with a random proportional rule. This probability distribution of the random proportional rule, which is similar to that of the stochastic universal sampling method generally applied to the selection operation in genetic algorithms, is good for exploring favorable paths in small traveling salesman problems (TSPs), but inefficient at exploring such paths in large TSPs. Therefore, this study presents the rank-based nonlinear selection pressure function, based on ranking of [τ (r, z)][η (r, z)]β, to improve the performance of the state transition rule of the original ACO, as well as a modified Q-learning method to solve reinforcement learning problems efficiently. The modified Q-learning method, which incorporates a constant pheromone trail distribution with standard Q-learning, can yield a solution effectively when applied in the local-updating rule of an ACO. In this article, the optimal settings for the control parameters (q) used in the rank-based selective pressure function and the discounted factor (γ) associated with modified Q-learning were investigated numerically using a benchmark St70 case of the static TSP. Furthermore, the improved ACO was applied to the static TSP of the KroA100 case and a route programing with 532 nodes. This study also applied the rank-based ACO to solve dynamic TSPs. By introducing the rank-based nonlinear selective pressure function and the modified Q-learning model into the original ACO, the presented rank-based ACO algorithm effectively explores paths affected by a change in the environment. In this work, the environment changes are traffic jams and road closures between cities, which sometimes force the salesman to change his route.