Ant Colony Optimization for traffic dispersion routing (original) (raw)
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— Ant Colony Optimization (ACO) has proven to be a very powerful optimization heuristic for combinatorial optimization problems. This paper introduces a new type of ACO algorithm that will be used for routing along multiple routes in a network as opposed to optimizing a single route. Contrary to traditional routing algorithms, the Ant Dispersion Routing (ADR) algorithm has the objective of determining recommended routes for every driver in the network, in order to increase network efficiency. We present the framework for the new ADR algorithm, as well as the design of a new cost function that translates the motivations and objectives of the algorithm. The proposed approach is illustrated with a small simulation-based case study for the Singapore Expressway Network.
Ant Colony Routing algorithm for freeway networks
Transportation Research Part C: Emerging Technologies, 2013
Dynamic traffic routing refers to the process of (re)directing vehicles at junctions in a traffic network according to the evolving traffic conditions. The traffic management center can determine desired routes for drivers in order to optimize the performance of the traffic network by dynamic traffic routing. However, a traffic network may have thousands of links and nodes, resulting in a large-scale and computationally complex nonlinear, non-convex optimization problem. To solve this problem, Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. ACO is implemented online to determine the control signal-i.e., the splitting rates at each node. However, using standard ACO for traffic routing is characterized by four main disadvantages: 1. traffic flows for different origins and destinations cannot be distinguished; 2. all ants may converge to one route, causing congestion; 3. constraints cannot be taken into account; and 4. neither can dynamic link costs. These problems are addressed by adopting a novel ACO algorithm with stench pheromone and with colored ants, called Ant Colony Routing (ACR). Using the stench pheromone, the ACR algorithm can distribute the vehicles over the traffic network with less or no traffic congestion, as well as reduce the number of vehicles near some sensitive zones, such as hospitals and schools. With colored ants, the traffic flows for multiple origins and destinations can be represented. The proposed approach is also implemented in a simulation-based case study in the Walcheren area, the Netherlands, illustrating the effectiveness of the approach.
A new ant colony routing approach with a trade-off between system and user optimum
2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2011
Dynamic traffic routing (DTR) refers to the process of (re)directing traffic at junctions in a traffic network corresponding to the evolving traffic conditions as time progresses. This paper considers the DTR problem for a traffic network defined as a directed graph, and deals with the mathematical aspects of the resulting optimization problem from the viewpoint of network flow theory. Traffic networks may have thousands of links and nodes, resulting in a sizable and computationally complex nonlinear, non-convex DTR optimization problem. To solve this problem Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. However, the standard ACO algorithm is not capable of solving the routing optimization problem aimed at the system optimum, and therefore a new ACO algorithm is developed to achieve the goal of finding the optimal distribution of traffic flows in the network.
Update Vehicle Traffic Routing Using Ant Colony Optimization Algorithm
2012
In this paper, the authors want to implement the solution of combinatorial problem, based on the heuristic behavior of ant. This paper focuses on a highly developed solution procedure using ACO algorithm. This helps to solve routing problems easily. It also reflects the method considering flow, distance, cost, and emergency etc. Here, a new algorithm named UVTR (Update Vehicle Traffic Routing) is represented to overcome the complexity of the previous algorithm. It yields the typical process for removing traffic problems in case of flow, distance, cost etc. This formulation is represented with systematic rules based case study for the Dhaka City.
As packets travel in a network, there needs to be an efficient routing algorithm to provide packets transport from source to destination with high bandwidth and lower routing delay time, this being a problem, the shortest path problem, the shortest and best route is the best solution to this kind of problem, anytime a packet transaction occurs in a network. The Ant Colony Optimization (ACO) is a heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization (CO) problems including the shortest path (SP) problem. In this project, the ant colony optimization (ACO) technique is used to find the shortest path (SP) in the routing problem of a network. The algorithm is uses two different metrics (bandwidth and routing delay) to determine the optimal route path (best route). The proposed method is used to determine the optimal path from source to destination. The Ant colony decision for any such routing problem must be made under the current network conditions, decisions that minimize delay and maximum bandwidth for each link (node) to selected optimal link.
An Improved Ant Colony Algorithm for the Shortest Path in City’s Road Network
Applied Mechanics and Materials, 2011
The shortest path between the start node and end node plays an important role in city’s road traffic network analysis system. The basic ant colony system algorithm which is a novel simulated evolutionary algorithm is studied to solve the shortest path problem. But the basic ant colony system algorithm is easy to run into the local optimum solution for shortest path. In order to solve the problem, the improved ant colony system algorithm is proposed. The improvement methods for selection strategy, local search, and information quantity modification of basic ant colony system are discussed in detail. The experiments are done in Beijing road network in China. The results of experiments show that comparing with the basic ant colony algorithm, the improved algorithm can easily converge at the global optimum for the shortest path.
Solving the Routing Problem by Ant Colony Optimization Algorithms
International Journal of Computing, 2016
The use of ant colony optimization algorithms for solving the routing problem in a process of products delivery taking into account a city transport infrastructure has shown in this research. The vehicle routing problem belongs to NP-hard task and its solution requires significant computing resources. Therefore, it is recommended to use metaheuristic methods to solve such problems including ant colony optimization algorithms. Solution of the Vehicle Routing Problem will cause a decrease of enterprises non-productive resources consumption and will promote the increase of their efficiency and competitiveness. The test example, consisting of eight consumers of freight and two transportation means with unlimited load capacity, moving around the certain city, is used for the implementation of the model. It can be further refined by taking into account various parameters besides transport infrastructure, including limitations on carrying capacity, a number of vehicles an working hours, an...
Cooperative Ant Colony Optimization in Traffic Route Calculations
2012
Ant Colony Optimization (ACO) algorithms tend to be isolated processes. When applying ACO principles to traffic route calculations, ants exploring the traffic network on behalf of a vehicle typically only perceive and apply pheromones related to that vehicle. Between ants exploring on behalf of different vehicles little cooperation exists. While such cooperation could improve the performance of the ACO algorithm, it is difficult to achieve because ants working on behalf of different vehicles are solving different problems.
Dynamic Travel Path Optimization System Using Ant Colony Optimization
2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, 2014
This paper demonstrates that ant colony optimization can efficiently improve the traffic situation in an urban environment. A Dynamic Travel Path Optimization System (DTPOS) based on Ant Colony Optimization (ACO) is proposed for the prediction of the best path to a given destination. In DTPOS, traffic factors such as average travel speed, average waiting time of cars and number of stopped cars in queue are taken into consideration. The proposed method is modeled in NetLogo. The simulation results demonstrate that the DTPOS model can greatly reduce the average travel time of cars in urban cases and improves the mean travel time by 47 percent when compared to similar models where the cars select their path without ACO. It has also been shown that the results can be further improved by 56 percent when the Previous Path Replacement (PPR) method is applied to the DTPOS results.
Route optimization via improved ant colony algorithm with graph network
International Journal of Reconfigurable and Embedded Systems (IJRES), 2023
Route optimization problem using vehicle routing problem (VRP) and time window constraint is explained as finding paths for a finite count of vehicles to provide service to a huge number of customers and hence, optimizing the path in a given duration of the time window. The vehicles in the loop have restricted intake of capacity. This path initiates from the depot, delivers the goods, and stops at the depot. Each customer is to serve exactly once. If the arrival of the vehicle is before the time window "opens" or when the time window "closes," there will be waiting for cost and late cost. The challenge involved over here is to scheduling visits to customers who are only available during specific time windows. Ant colony optimization (ACO) algorithm is a meta-heuristic algorithm stimulated by the growing behaviour of real ants. In this paper, we combine the ACO algorithm with graph network henceforth increasing the number of vehicles in a particular depot for increasing the efficiency for timely delivery of the goods in a particular time width. This problem is solved by, an efficient technique known as the ACO+graph algorithm.