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.

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.

scholarly journals Hierarchical Sarsa Learning Based Route Guidance Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Feng Wen ◽  
Xingqiao Wang ◽  
Xiaowei Xu
Keyword(s):  
State Space ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Modern Society ◽  
Guidance System ◽  
Route Guidance ◽  
Guidance Algorithm ◽  
Reinforcement Learning Models ◽  
Dynamic Route

In modern society, route guidance problems can be found everywhere. Reinforcement learning models can be normally used to solve such kind of problems; particularly, Sarsa Learning is suitable for tackling with dynamic route guidance problem. But how to solve the large state space of digital road network is a challenge for Sarsa Learning, which is very common due to the large scale of modern road network. In this study, the hierarchical Sarsa learning based route guidance algorithm (HSLRG) is proposed to guide vehicles in the large scale road network, in which, by decomposing the route guidance task, the state space of route guidance system can be reduced. In this method, Multilevel Network method is introduced, and Differential Evolution based clustering method is adopted to optimize the multilevel road network structure. The proposed algorithm was simulated with several different scale road networks; the experiment results show that, in the large scale road networks, the proposed method can greatly enhance the efficiency of the dynamic route guidance system.

The Research on Vehicle Routing Problem Based on Improved Ant Colony Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 2439-2446 ◽  
Author(s):  
Zheng Qiang Jiang ◽  
Yue Wu
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Vital Role ◽  
Ant Colony ◽  
Transition Rule ◽  
Routing Problem ◽  
Np Hard Problem ◽  
Matlab Programming ◽  
Improved Ant Colony Algorithm

Vehicle Routing Problem (VRP) plays a vital role in mathematical and logistics research. Its a typical NP-Hard problem, and ant colony algorithm has been proven to be an effect way in solving these problems. An improved ant colony algorithm is proposed to solve VRP on the basis of depth analyzing VRP and ant colony algorithm. It proposes the mathematical model of VRP and designs an improved ant colony algorithm, considering the inefficient solving, easy to partial stagnation shortcomings of ant colony algorithm and the attraction, repulsion, time constraint phenomena in VRP. This paper redesigns the pheromone update rule and the state transition rule and then it makes a simulation experiment by MATLAB programming, the results show that the improved ant colony is feasible and very valid in solving VRP.

Study on Fast Codeword Search Based on Improved Ant Colony Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3768-3773
Author(s):  
Da Yong Zou ◽  
Wei Wu
Keyword(s):  
Vector Quantization ◽  
Ant Colony Algorithm ◽  
Behavior Pattern ◽  
Optimal Path ◽  
Search Time ◽  
Search Space ◽  
Ant Colony ◽  
Computation Complexity ◽  
Setting Parameters ◽  
Quick Search

Vector quantization technology is an efficient and competitive method for data compression, but it is not easy to be implemented because of the comparatively high computation complexity it requires during the coding and decoding process. This paper presents a method of Dual Population Ant Colony Algorithm Codeword Quick Search (DPACAS), exploiting the mechanism of ant trace the optimal path through the pheromones remained, and the behavior pattern of making objects together by picking up and putting down them. It uses Parallel Ant Colony algorithm to sufficiently accelerate the convergence of the ant colony. When the scale of the codebook becomes larger, by setting parameters reasonably and exchanging the pheromones between two species, it broadens the search space, reduces the search time and improves the algorithmic global convergence effectively.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

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