Intelligent Traffic Light Model Based on Grey-Markov Model and Improved Ant Colony Optimization for Dynamic Route Guidance

2015 ◽  
Author(s):  
Jiaxu Zhao ◽  
Zhide Chen ◽  
Yali Zeng
Keyword(s):  
Markov Model ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Route Guidance ◽  
Traffic Light ◽  
Model Based ◽  
Dynamic Route

Ant Colony Optimization and image model-based robot manipulator system for pick-and-place tasks

2019 ◽  
Vol 36 (2) ◽  
pp. 1083-1098
Author(s):  
Ching-Chang Wong ◽  
Hsuan-Ming Feng ◽  
Yu-Cheng Lai ◽  
Chia-Jun Yu
Keyword(s):  
Ant Colony Optimization ◽  
Robot Manipulator ◽  
Ant Colony ◽  
Image Model ◽  
Model Based ◽  
Pick And Place

Study on Improved Ant Colony Algorithm in Dynamic Multi-Paths Route Guidance System

2010 ◽  
Vol 20-23 ◽  
pp. 243-248 ◽  
Author(s):  
Jun Hua Gu ◽  
En Hai Liu ◽  
Yan Liu Liu ◽  
Na Zhang
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Shortest Paths ◽  
Weight Coefficient ◽  
Ant Colony ◽  
Guidance System ◽  
Transition Rule ◽  
Route Guidance ◽  
Aggregative Response ◽  
Dynamic Route

The traditional Dynamic Route Guidance System (DRGS) provides only the optimal path to the travelers, which may easily lead to aggregative response of the travelers and overcrowding drift. This paper presents an approach based on Ant Colony Optimization (ACO) for solving the k-shortest paths problem in DRGS. In order to improve the convergence rate, the basic ACO is improved by introducing direction function the weight coefficient of which can be adjusted to vary state transition rule and standardized transformation to eliminate the influence of the size and dimension of pheromone and heuristic information. Compared with basic ACO, simulation experiments indicate that the improved ACO is more effective and efficient.

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