scholarly journals Parallel Ant Colony Algorithm for Shortest Path Problem

2019 ◽  
Author(s):  
Géza Katona ◽  
Balázs Lénárt ◽  
János Juhász
Keyword(s):  
Parallel Computing ◽  
Shortest Path ◽  
Ant Colony Algorithm ◽  
State Of The Art ◽  
Shortest Path Problem ◽  
Ant Colony ◽  
Parameter Study ◽  
Real Network

During travelling, more and more information must be taken into account, and travelers have to make several complex decisions. In order to support these decisions, IT solutions are unavoidable, and as the computational demand is constantly growing, the examination of state-of-the-art methodologies is necessary. In our research, a parallelized Ant Colony algorithm was investigated, and a parameter study on a real network has been made. The aim was to inspect the sensibility of the method and to demonstrate its applicability in a multi-threaded system (e.g. Cloud-based systems). Based on the research, increased effectiveness can be reached by using more threads. The novelty of the paper is the usage of the processors’ parallel computing capability for routing with the Ant Colony algorithm.

SH: A Novel Method for the Dynamic and Shortest Path Problem

2010 ◽  
Vol 129-131 ◽  
pp. 1013-1017
Author(s):  
Ya Fei Guo ◽  
Zheng Qin ◽  
Rong Hua Guo ◽  
Lei Ji
Keyword(s):  
Shortest Path ◽  
Heuristic Search ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Real Life ◽  
Shortest Path Problem ◽  
Ant Colony ◽  
Current Path ◽  
Novel Method ◽  
Better Than

For the dynamic and shortest path problem, a novel algorithm SH(simulate human) is designed by simulating the process of our searching path in real life. The algorithm adopts the idea of heuristic search and integrates with the ant colony algorithm, in which the saved current path, the idea of “ask once every junction”, the bypassing barrier search and other some related definitions are proposed, as well as the ant colony algorithm is improved, so as to find the better solution and reduce the searching time. The experimental results show that the algorithm runs better than other existing methods. Moreover, it can find the shortest path or the approximate shortest one in a shorter time on road networks of any scales. Especially, SH algorithm is more effective for the large scale road network.

An Improved Ant Colony Algorithm for the Shortest Path in City’s Road Network

2011 ◽  
Vol 121-126 ◽  
pp. 1296-1300 ◽  
Author(s):  
Jun Bi ◽  
Jie Zhang ◽  
Wen Le Xu
Keyword(s):  
Shortest Path ◽  
Road Network ◽  
Ant Colony Algorithm ◽  
Road Traffic ◽  
Global Optimum ◽  
Ant Colony ◽  
Selection Strategy ◽  
Ant Colony System ◽  
Local Optimum ◽  
Information Quantity

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.

Approach to Find Shortest Path Using Ant Colony Algorithm

2018 ◽  
pp. 1243-1252 ◽  
Author(s):  
Mudasar Basha ◽  
M. Siva Kumar ◽  
Vemulapalli Sai Pranav ◽  
B. Khaleelu Rehman
Keyword(s):  
Shortest Path ◽  
Ant Colony Algorithm ◽  
Ant Colony

Effective ACPS-Based Rescheduling of Parallel Batch Processing Machines with MapReduce

2014 ◽  
Vol 575 ◽  
pp. 820-824
Author(s):  
Bin Zhang ◽  
Jia Jin Le ◽  
Mei Wang
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Ant Colony ◽  
Search Problem ◽  
Parallel Search ◽  
Good Efficiency ◽  
Heuristic Scheduling ◽  
Low Efficiency

MapReduce is a highly efficient distributed and parallel computing framework, allowing users to readily manage large clusters in parallel computing. For Big data search problem in the distributed computing environment based on MapReduce architecture, in this paper we propose an Ant colony parallel search algorithm (ACPSMR) for Big data. It take advantage of the group intelligence of ant colony algorithm for global parallel search heuristic scheduling capabilities to solve problem of multi-task parallel batch scheduling with low efficiency in the MapReduce. And we extended HDFS design in MapReduce architecture, which make it to achieve effective integration with MapReduce. Then the algorithm can make the best of the scalability, high parallelism of MapReduce. The simulation experiment result shows that, the new algorithm can take advantages of cloud computing to get good efficiency when mining Big data.

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