A Multimetric Ant Colony Optimization Algorithm for Dynamic Path Planning in Vehicular Networks

Wang, Zhen; Li, Jianqing; Fang, Manlin; Li, Yang

doi:10.1155/2015/271067

Cited by 8 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pontryagin's maximum principle can discover the optimal driving regimes, but it must determine the optimal sequence of these regimes and the switching points between them. In addition to the indirect approaches described above, some researchers have used heuristic algorithms [18][19][20][21][22][23][24][25]. However, these metaheuristics are not guaranteed for the existence of globally optimal solutions.…”

Section: Energy-efficient Train Control For Wheel-rail Systemsmentioning

confidence: 99%

Energy‐efficient operation of medium‐speed maglev through integrated traction and train control

Lai,

Liu,

Wang

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

In contrast to the wheel‐track trains, where the motor characteristics are considered a constant value, the motor characteristics of the medium‐speed maglev (MSM) trains are the dependent variable of the position. This article studies the integration of traction power control and train control for the MSM to minimize energy consumption. First, an innovative integrated energy‐efficient optimization model for MSM train control is constructed, considering the characteristics of the linear motors. Then, a multi‐level dynamic programming (DP) approach, which includes the train operation simulation with the linear motor, is developed to solve the optimization problem. Furthermore, a speed‐up strategy for the DP approach is proposed by a pre‐calculated target train speed band (TTSB). The results of numerical experiments show that the DP approach yields a more practical train speed profile. In contrast, the DP approach with the TTSB strategy can achieve a better trade‐off between solution accuracy and computational efficiency.

show abstract

Section: Energy-efficient Train Control For Wheel-rail Systemsmentioning

confidence: 99%

Energy‐efficient operation of medium‐speed maglev through integrated traction and train control

Lai,

Liu,

Wang

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…However, the variation in probability distribution with respect to different iterations limited the detection accuracy. Wang et al (2015) proposed the multi-metric ant colony optimization algorithm, an ant colony VTR system, which utilizes the least distance and time, optimal road situation, or the combined sequence, suited to users' preferences (pheromones), to consider real-time data for the VTR process. The algorithm uses the TOPSIS algorithm to select the optimal route.…”

Section: Ant Algorithmsmentioning

confidence: 99%

Intelligent Traffic Management: A Review of Challenges, Solutions, and Future Perspectives

Ravish

Swamy²

2021

Transport and Telecommunication Journal

View full text Add to dashboard Cite

Recent years have witnessed a colossal increase of vehicles on the roads; unfortunately, the infrastructure of roads and traffic systems has not kept pace with this growth, resulting in inefficient traffic management. Owing to this imbalance, traffic jams on roads, congestions, and pollution have shown a marked increase. The management of growing traffic is a major issue across the world. Intelligent Transportation Systems (ITS) have a great potential in offering solutions to such issues by using novel technologies. In this review, the ITS-based solutions for traffic management and control have been categorized as traffic data collection solutions, traffic management solutions, congestion avoidance solutions, and travel time prediction solutions. The solutions have been presented along with their underlying technologies, advantages, and drawbacks. First, important solutions for collecting traffic-related data and road conditions are discussed. Next, ITS solutions for the effective management of traffic are presented. Third, key strategies based on machine learning and computational intelligence for avoiding congestion are outlined. Fourth, important solutions for accurately predicting travel time are presented. Finally, avenues for future work in these areas are discussed.

show abstract

“…In the process of searching for food, ants will release a certain amount of pheromones in their path, and the ant colony uses these pheromones to communicate with each other. When more and more ants pass through a certain path, the pheromone concentration of this path will be higher, and other ants will have a greater probability to choose this path, which plays a positive feedback role, but it is also easy to lead to the occurrence of local optimum or deadlock [20].…”

Section: Environmentalmentioning

confidence: 99%

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Wang

Zhao

et al. 2021

Journal of Robotics

View full text Add to dashboard Cite

Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

show abstract

A Multimetric Ant Colony Optimization Algorithm for Dynamic Path Planning in Vehicular Networks

Cited by 8 publications

References 19 publications

Energy‐efficient operation of medium‐speed maglev through integrated traction and train control

Energy‐efficient operation of medium‐speed maglev through integrated traction and train control

Intelligent Traffic Management: A Review of Challenges, Solutions, and Future Perspectives

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Contact Info

Product

Resources

About