Using GPU and OpenACC to Accelerate the Maze Optimal Routing Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1338-1341
Author(s):  
Yu Liu ◽  
Yi Xiao
Keyword(s):  
Ant Colony Algorithm ◽  
Programming Model ◽  
Routing Algorithm ◽  
Operation Time ◽  
Ant Colony ◽  
Gpu Acceleration ◽  
Optimal Routing ◽  
Task Mapping ◽  
Routing Problem ◽  
High Level

in order to improve the efficiency of maze optimal routing problem, a GPU acceleration programming model OpenACC is used in this paper. By analyzing an algorithm which solves the maze problem based on ant colony algorithm, we complete the task mapping on the model. Though GPU acceleration, ant colony searching process was changed into parallel matrix operations. To decrease the algorithm accessing overhead and increase operating speed, data were rationally organized and stored for GPU. Experiments of different scale maze matrix show that the parallel algorithm greatly reduces the operation time. Speedup will be increased with the expansion of the matrix size. In our experiments, the maximum speedup is about 6.1. The algorithm can solve larger matrices with a high level of processing performance by adding efficient OpenACC instruction to serial code and organizing the data structure for parallel accessing.

scholarly journals Quantum Max-min Ant Colony Algorithm For QoS Routing Problem

2021 ◽  
Vol 1827 (1) ◽  
pp. 012163
Author(s):  
Yalong Li ◽  
Wang Wei ◽  
Sun Jing ◽  
Wan Jie
Keyword(s):  
Ant Colony Algorithm ◽  
Qos Routing ◽  
Ant Colony ◽  
Routing Problem

scholarly journals A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

2021 ◽  
Author(s):  
Hongguang Wu ◽  
Yuelin Gao ◽  
Wanting Wang ◽  
Ziyu Zhang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Ant Colony ◽  
Routing Problem ◽  
Multiple Strategies ◽  
Hard Time ◽  
Mutation Operation

AbstractIn this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.

Research on MANET Routing Algorithm Based on Ant Colony Algorithm

2011 ◽  
Vol 135-136 ◽  
pp. 781-787
Author(s):  
Yong Feng Ju ◽  
Hui Chen
Keyword(s):  
Network Lifetime ◽  
Ant Colony Algorithm ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Packet Transmission ◽  
Ant Colony ◽  
Dynamic Allocation ◽  
Routing Table ◽  
Manet Routing ◽  
Rate Dynamic

This paper proposed a new Ad Hoc dynamic routing algorithm, which based on ant-colony algorithm in order to reasonably extend the dynamic allocation of network traffic and network lifetime. The Algorithm choose path according transmission latency, path of the energy rate, congestion rate, dynamic rate. The Algorithm update the routing table by dynamic collection of path information after path established. The analyse shows that algorithm increases the network throughput, reduces the average end-to-end packet transmission latency, and extends the network lifetime, achieves an improving performance.

scholarly journals High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

2021 ◽  
Author(s):  
Breno A. de Melo Menezes ◽  
Nina Herrmann ◽  
Herbert Kuchen ◽  
Fernando Buarque de Lima Neto
Keyword(s):  
Ant Colony Optimization ◽  
High Performance ◽  
Optimization Problems ◽  
Programming Model ◽  
Parallel Implementation ◽  
Ant Colony ◽  
Algorithmic Skeletons ◽  
Low Level ◽  
Programming Patterns ◽  
High Level

AbstractParallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations.

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