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Comparative evaluation of platforms for parallel Ant Colony Optimization

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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.

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This work is jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grant 15290/PI/2010, by the Spanish MEC and European Commission FEDER under grant TIN2012-31345, by the UCAM under grant PMAFI/26/12, by the Junta de Andalucía under Project of Excellence P12-TIC-1741 and by the supercomputing infrastructure of the NLHPC (ECM-02). We also thank NVIDIA for hardware donation under CUDA Teaching Center 2011-14, CUDA Research Center 2012-14 and CUDA Fellow 2012-14 Awards.

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Correspondence to José M. Cecilia.

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Guerrero, G.D., Cecilia, J.M., Llanes, A. et al. Comparative evaluation of platforms for parallel Ant Colony Optimization. J Supercomput 69, 318–329 (2014).

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