Comparative evaluation of platforms for parallel Ant Colony Optimization

Gines D. Guerrero, Jose M. Cecilia, Antonio Llanes, Jose M. Garcia, Martyn Amos, Manuel Ujaldon

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)318-329
JournalJournal of Supercomputing
Volume69
Issue number1
Early online date18 Mar 2014
DOIs
Publication statusPublished - Jul 2014

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