scholarly journals Dynamic Path Planning Based on Improved Ant Colony Algorithm in Traffic Congestion

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 180773-180783
Author(s):  
Chunjiang Wu ◽  
Shijie Zhou ◽  
Licai Xiao
Keyword(s):  
Path Planning ◽  
Traffic Congestion ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Improved Ant Colony Algorithm

Path Planning for Mobile Robot Based on Improved Ant Colony Algorithm

2013 ◽  
Vol 385-386 ◽  
pp. 717-720 ◽  
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Probability Function ◽  
Ant Colony ◽  
Local Optimum ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Improved Ant Colony Algorithm ◽  
Improved Algorithm

This paper presents a dynamic path planning method based on improved ant colony algorithm. In order to increasing the algorithm’s convergence speed and avoiding to fall into local optimum, we propose adaptive migratory probability function and updating the pheromone. We apply the improved algorithm to path planning for mobile robot and the simulation experiment proved that improved algorithm is viable and efficient.

scholarly journals Path Planning Based on an Improved Ant Colony Algorithm

2018 ◽  
Vol 228 ◽  
pp. 01010
Author(s):  
Miaomiao Wang ◽  
Zhenglin Li ◽  
Qing Zhao ◽  
Fuyuan Si ◽  
Dianfang Huang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Pheromone Concentration ◽  
Suppression Strategy ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

The classical ant colony algorithm has the disadvantages of initial search blindness, slow convergence speed and easy to fall into local optimum when applied to mobile robot path planning. This paper presents an improved ant colony algorithm in order to solve these disadvantages. First, the algorithm use A* search algorithm for initial search to generate uneven initial pheromone distribution to solve the initial search blindness problem. At the same time, the algorithm also limits the pheromone concentration to avoid local optimum. Then, the algorithm optimizes the transfer probability and adopts the pheromone update rule of "incentive and suppression strategy" to accelerate the convergence speed. Finally, the algorithm builds an adaptive model of pheromone coefficient to make the pheromone coefficient adjustment self-adaptive to avoid falling into a local minimum. The results proved that the proposed algorithm is practical and effective.

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