A parallel ant colony optimization to globally optimize area in high-level synthesis. (c2011)

2011 ◽  
Author(s):  
David Elias Kozhaya
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
High Level Synthesis ◽  
High Level

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.

scholarly journals Dynamic Vehicle Routing Problems with Enhanced Ant Colony Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Haitao Xu ◽  
Pan Pu ◽  
Feng Duan
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Search Space ◽  
Ant Colony ◽  
Local Optimum ◽  
Dynamic Vehicle Routing ◽  
Routing Problem ◽  
High Level

As we all know, there are a great number of optimization problems in the world. One of the relatively complicated and high-level problems is the vehicle routing problem (VRP). Dynamic vehicle routing problem (DVRP) is a major variant of VRP, and it is closer to real logistic scene. In DVRP, the customers’ demands appear with time, and the unserved customers’ points must be updated and rearranged while carrying out the programming paths. Owing to the complexity and significance of the problem, DVRP applications have grabbed the attention of researchers in the past two decades. In this paper, we have two main contributions to solving DVRP. Firstly, DVRP is solved with enhanced Ant Colony Optimization (E-ACO), which is the traditional Ant Colony Optimization (ACO) fusing improved K-means and crossover operation. K-means can divide the region with the most reasonable distance, while ACO using crossover is applied to extend search space and avoid falling into local optimum prematurely. Secondly, several new evaluation benchmarks are proposed, which can objectively and comprehensively estimate the proposed method. In the experiment, the results for different scale problems are compared to those of previously published papers. Experimental results show that the algorithm is feasible and efficient.

scholarly journals Selection of the optimal set of versions of N-version software using the ant colony optimization

2021 ◽  
Vol 2094 (3) ◽  
pp. 032026
Author(s):  
A Aljarbouh
Keyword(s):  
Ant Colony Optimization ◽  
Software Reliability ◽  
Ant Colony ◽  
Software Components ◽  
High Level ◽  
Optimal Set ◽  
Selection Of

Abstract The article discusses the problem of ensuring a high level of software reliability. A software reliability high level can be ensured using N-version programming. N-version software is distinguished by the software components redundancy, designed to solve one problem using different methods. Software redundancy requires additional resources. The challenge is thus to increase the software reliability, while at the same time minimising the resources used. In this article, ant colony optimization is used to solve the problem of choosing the optimal set of versions of N-version software.

Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT

2012 ◽  
Author(s):  
Earth B. Ugat ◽  
Jennifer Joyce M. Montemayor ◽  
Mark Anthony N. Manlimos ◽  
Dante D. Dinawanao
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Timetabling Problem ◽  
University Course ◽  
The University

Achievement of Cost-Effective Datasets Transference in The Cloud Through Ant Colony Optimization

2012 ◽  
Vol 3 (3) ◽  
pp. 122-125
Author(s):  
THAHASSIN C THAHASSIN C ◽  
◽  
A. GEETHA A. GEETHA ◽  
RASEEK C RASEEK C
Keyword(s):  
Ant Colony Optimization ◽  
Cost Effective ◽  
Ant Colony

Floorplan-Aware High-Level Synthesis for Generalized Distributed-Register Architectures

2009 ◽  
Vol E92-A (12) ◽  
pp. 3169-3179 ◽  
Author(s):  
Akira OHCHI ◽  
Nozomu TOGAWA ◽  
Masao YANAGISAWA ◽  
Tatsuo OHTSUKI
Keyword(s):  
High Level Synthesis ◽  
High Level
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