Comparative evaluation of platforms for parallel Ant Colony Optimization

The rapidly growing field of nature-inspired computing concerns the development and application of algorithms and methods based on biological or physical principles. This approach is particularly compelling for practitioners in high-performance computing, as natural algorithms are often inherently p...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of supercomputing 2014-07, Vol.69 (1), p.318-329
Main Authors: Guerrero, Ginés D., Cecilia, José M., Llanes, Antonio, García, José M., Amos, Martyn, Ujaldón, Manuel
Format: Article
Language:English
Subjects:
Algorithms
Ant colony optimization
Benchmarking
Compilers
Computation
Computer Science
Hardware
Interpreters
Mathematical analysis
Microprocessors
Platforms
Processor Architectures
Programming Languages
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The rapidly growing field of nature-inspired computing concerns the development and application of algorithms and methods based on biological or physical principles. This approach is particularly compelling for practitioners in high-performance computing, as natural algorithms are often inherently parallel in nature (for example, they may be based on a “swarm”-like model that uses a population of agents to optimize a function). Coupled with rising interest in nature-based algorithms is the growth in heterogenous computing ; systems that use more than one kind of processor. We are therefore interested in the performance characteristics of nature-inspired algorithms on a number of different platforms. To this end, we present a new OpenCL-based implementation of the Ant Colony Optimization algorithm, and use it as the basis of extensive experimental tests. We benchmark the algorithm against existing implementations, on a wide variety of hardware platforms, and offer extensive analysis. This work provides rigorous foundations for future investigations of Ant Colony Optimization on high-performance platforms.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-014-1154-5