A Multi-Weights Ant Colony Algorithm for Solving Optimal Path in Tourism

2014 ◽  
Vol 998-999 ◽  
pp. 789-792 ◽  
Author(s):  
Yu Zhong Liu ◽  
Hua Ping Yu ◽  
Bing Huang ◽  
Yuan Fang Zhang
Keyword(s):  
High Performance ◽  
Ant Colony Algorithm ◽  
Fundamental Problem ◽  
Optimal Path ◽  
Influence Factors ◽  
Path Selection ◽  
Ant Colony ◽  
Route Selection ◽  
Route Length ◽  
Relevant Factors

Optimal path selection is a fundamental problem in tourism, the influence factors of which only including the rout length, but also including weather, transportation and the scenery of attractions and other relevant factors. Therefore, route selection only based on the route length cannot capture the actual requirement. The paper studies the multi-weights (such as weather, route length, attractions scenery and etc.) in route selection, and then proposed an improved ant colony algorithm based on multi-weights (ACA-MW), which uses the multi-weights ant and the genetic variation to search optimal path. Simulated experiment of the ACA-MW shows high performance, the improved algorithm is effective. In tourism, ACA-MW can do well in optimal path selection problem.

Optimal path selection for logistics transportation based on an improved ant colony algorithm

2020 ◽  
Vol 13 (2) ◽  
pp. 200
Author(s):  
Xiangqian Wang ◽  
Huizong Li ◽  
Jie Yang ◽  
Chaoyu Yang ◽  
Haixia Gui
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Path Selection ◽  
Ant Colony ◽  
Selection For ◽  
Improved Ant Colony Algorithm

scholarly journals Optimal path selection of multimodal transport based on Ant Colony Algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032011
Author(s):  
Dayun Ge
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Minimum Cost ◽  
Transportation Cost ◽  
Path Selection ◽  
Transport Network ◽  
Ant Colony ◽  
Practical Significance ◽  
Container Transportation ◽  
Multimodal Transport

Abstract In the process of multimodal transport, the cost and time of transportation are particularly important. In order to avoid unreasonable container transportation and unnecessary waste of transportation capacity and transportation cost, we must effectively integrate the advantages of various transportation modes, select the most suitable transportation mode and the most reasonable transportation path, and take the minimum cost and time as the goal to ensure the smooth transportation of goods to the destination. Therefore, optimizing multimodal transport network has very important practical significance. This paper starts with the multimodal transport network under a single task, designs the solution method of the model combined with ant colony algorithm, and gives an example. Finally, the model and algorithm design are proved to be reasonable by using MATLAB solution algorithm.

Optimal Path Selection for Mine Emergency Rescue Based on Improved Ant Colony Algorithm

2010 ◽  
Vol 159 ◽  
pp. 168-171
Author(s):  
Feng Tian ◽  
Zhong Zhao Chen
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Lower Solution ◽  
Path Selection ◽  
Ant Colony ◽  
Emergency Rescue ◽  
Local Optimal Solution ◽  
Solution Accuracy ◽  
Improved Ant Colony Algorithm

It is the primary task to ensure the safety of lives and property of underground workers with the increasing amount of coal mining. Under the actual complex environment of coal, all kinds of uncertian factors should be considered except for the distance for the selection of the optimal path to reduce casualties. Aiming at the defect of the lower solution accuracy and tending to fall into local optimal solution of the basic ant colony algorithm(ACA), an improved ant colony algorithm is presented based on the model of ACA. Experiment results show that the new algorithm can get better results.

Mobile Robot Path Planning Based on Ant Colony Optimization

2014 ◽  
Vol 687-691 ◽  
pp. 706-709
Author(s):  
Bao Feng Zhang ◽  
Ya Chun Wang ◽  
Xiao Ling Zhang
Keyword(s):  
Path Planning ◽  
High Performance ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Infrared Sensors ◽  
Object Based ◽  
Starting Point ◽  
Robot Path

Global path planning is quoted in this paper. The stoical and global environment has been given to us, which is abstracted with grid method before we build the workspace model of the robot. With the adoption of the ant colony algorithm, the robot tries to find a path which is optimal or optimal-approximate path from the starting point to the destination. The robot with the built-in infrared sensors navigates autonomously to avoid collision the optimal path which has been built, and moves to the object. Based on the MATLAB platform, the simulation results indicate that the algorithm is rapid, simple, efficient and high-performance. Majority of traditional algorithms of the path planning have disadvantages, for instance, the method of artificial potential field is falling into the problem of local minimum value easily. ACO avoids these drawbacks, therefore the convergence period can be extended, and optimal path can be planned rapidly.

scholarly journals Research on the Uncertainty Decision Model of the Regional Collaborative Innovation System Based on an Improved Ant Colony Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Xiaona Zhang ◽  
Fayin Wang
Keyword(s):  
Decision Model ◽  
Ant Colony Algorithm ◽  
Innovation System ◽  
Path Selection ◽  
Collaborative Optimization ◽  
Ant Colony ◽  
Evaluation Index System ◽  
Regional Innovation ◽  
Collaborative Innovation ◽  
Improved Ant Colony Algorithm

The regional collaborative innovation system is a nonlinear complex system, which has obvious uncertainty characteristics in the aspects of member selection and evolution. Ant colony algorithm, which can do the uncertainty collaborative optimization decision-making, is an effective tool to solve the uncertainty decision path selection problem. It can improve the cooperation efficiency of each subsystem and achieve the goal of effective cooperation. By analysing the collaborative evolution mechanisms of the regional innovation system, an evaluation index system for the regional collaborative innovation system is established considering the uncertainty of collaborative systems. The collaborative uncertainty decision model is constructed to determine the regional innovation system’s collaborative innovation effectiveness. The improved ant colony algorithm with the pheromone evaporation model is applied to traversal optimization to identify the optimal solution of the regional collaborative innovation system. The collaboration capabilities of the ant colony include pheromone diffusion so that local updates are more flexible and the result is more rational. Finally, the method is applied to the regional collaborative innovation system.

Cutting trajectory planning of sections with complex composition for roadheader

2018 ◽  
Vol 233 (4) ◽  
pp. 1441-1452 ◽  
Author(s):  
Suyu Wang ◽  
Miao Wu
Keyword(s):  
Trajectory Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Complex Composition ◽  
Cutting Head ◽  
Pheromone Concentration ◽  
Simulation Results

In order to realize the autonomous cutting for tunneling robot, the method of cutting trajectory planning of sections with complex composition was proposed. Firstly, based on the multi-sensor parameters, the existence, the location, and size of the dirt band were determined. The roadway section environment was modeled by grid method. Secondly, according to the cutting process and tunneling cutting characteristics, the cutting trajectory ant colony algorithm was proposed. To ensure the operation safety and avoid the cutting head collision, the expanding operation was adopt for dirt band, and the aborting strategy for the ants trapped in the local optimum was put forward to strengthen the pheromone concentration of the found path. The simulation results showed that the proposed method can be used to plan the optimal cutting trajectory. The ant colony algorithm was used to search for the shortest path to avoid collision with the dirt band, and the S-path cutting was used for the left area to fulfill section forming by following complete cover principle. All the ants have found the optimal path within 50 times iteration of the algorithm, and the simulation results were better than particle swarm optimization and basic ant colony optimization.

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